eBay:iPads:: startprice regression:: spdiff_sp
bdanalytics
Date: (Tue) Jul 28, 2015
Introduction:
Data: Source: Training: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv
New: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Regression results: First run:
Classification results: template: prdline.my == “Unknown” -> 296 Low.cor.X.glm: Leaderboard: 0.83458 newobs_tbl=[N=471, Y=327]; submit_filename=template_Final_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=76]=201; max.Accuracy.OOB=0.7710; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=95.42; productline=49.22; D.T.like=29.75; D.T.use=26.32; D.T.box=21.53;
prdline: -> Worse than template prdline.my == “Unknown” -> 285 All.X.no.rnorm.rf: Leaderboard: 0.82649 newobs_tbl=[N=485, Y=313]; submit_filename=prdline_Final_rf_submit.csv OOB_conf_mtrx=[YN=119, NY=80]=199; max.Accuracy.OOB=0.8339; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=84.25; D.sum.TfIdf=7.28; D.T.use=4.26; D.T.veri=2.78; D.T.scratch=1.99; D.T.box=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.81234 newobs_tbl=[N=471, Y=327]; submit_filename=prdline_Low_cor_X_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=74]=199; max.Accuracy.OOB=0.8205; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=96.07; prdline.my=51.37; D.T.like=29.39; D.T.use=25.43; D.T.box=22.27; D.T.veri=; D.T.scratch=;
oobssmpl: -> Low.cor.X.glm: Leaderboard: 0.83402 newobs_tbl=[N=440, Y=358]; submit_filename=oobsmpl_Final_glm_submit OOB_conf_mtrx=[YN=114, NY=84]=198; max.Accuracy.OOB=0.7780; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=93.87; prdline.my=60.48; D.sum.TfIdf=; D.T.condition=8.69; D.T.screen=7.96; D.T.use=7.50; D.T.veri=; D.T.scratch=;
category: -> Low.cor.X.glm: Leaderboard: 0.82381 newobs_tbl=[N=470, Y=328]; submit_filename=category_Final_glm_submit OOB_conf_mtrx=[YN=119, NY=57]=176; max.Accuracy.OOB=0.8011; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=79.19; prdline.my=55.22; D.sum.TfIdf=; D.T.ipad=27.05; D.T.like=21.44; D.T.box=20.67; D.T.condition=; D.T.screen=;
dataclns: -> All.X.no.rnorm.rf: Leaderboard: 0.82211 newobs_tbl=[N=485, Y=313]; submit_filename=dataclns_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=75]=179; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=65.85; prdline.my=7.74; D.sum.TfIdf=; D.T.use=2.01; D.T.condition=1.87; D.T.veri=1.62; D.T.ipad=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.79264 newobs_tbl=[N=460, Y=338]; submit_filename=dataclns_Low_cor_X_glm_submit OOB_conf_mtrx=[YN=113, NY=74]=187; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 -> different from prev run of 0.6 biddable=100.00; startprice.log=91.85; prdline.my=38.34; D.sum.TfIdf=; D.T.ipad=29.92; D.T.box=27.76; D.T.work=25.79; D.T.use=; D.T.condition=;
txtterms: -> top_n = c(10) Low.cor.X.glm: Leaderboard: 0.81448 newobs_tbl=[N=442, Y=356]; submit_filename=txtterms_Final_glm_submit OOB_conf_mtrx=[YN=113, NY=69]=182; max.Accuracy.OOB=0.7943; opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=90.11; prdline.my=37.65; D.sum.TfIdf=; D.T.ipad=28.67; D.T.work=24.90; D.T.great=21.44; # [1] “D.T.condit” “D.T.condition” “D.T.good” “D.T.ipad” “D.T.new”
# [6] “D.T.scratch” “D.T.screen” “D.T.this” “D.T.use” “D.T.work”
All.X.glm: Leaderboard: 0.81016
newobs_tbl=[N=445, Y=353]; submit_filename=txtterms_Final_glm_submit
OOB_conf_mtrx=[YN=108, NY=72]=180; max.Accuracy.OOB=0.7966;
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.log=88.24; prdline.my=33.81; D.sum.TfIdf=;
D.T.scratch=25.51; D.T.use=18.97; D.T.good=16.37; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.great” “D.T.excel” “D.T.work” “D.T.ipad”
Max.cor.Y.rpart: Leaderboard: 0.79258
newobs_tbl=[N=439, Y=359]; submit_filename=txtterms_Final_rpart_submit
OOB_conf_mtrx=[YN=105, NY=76]=181; max.Accuracy.OOB=0.7954802;
opt.prob.threshold.OOB=0.5
startprice.log=100; biddable=; prdline.my=; D.sum.TfIdf=;
D.T.scratch=; D.T.use=; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.80929
newobs_tbl=[N=545, Y=253]; submit_filename=txtterms_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=61]=169; max.Accuracy.OOB=0.8090395
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=78.82; idseq.my=63.43; prdline.my=45.57;
D.T.use=2.76; D.T.condit=2.35; D.T.scratch=2.00; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
txtclstr: All.X.no.rnorm.rf: Leaderboard: 0.79363 -> 0.79573 newobs_tbl=[N=537, Y=261]; submit_filename=txtclstr_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=61]=165; max.Accuracy.OOB=0.8135593 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=79.99; idseq.my=64.94; prdline.my=4.14; prdline.my.clusterid=1.15; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
dupobs: All.X.no.rnorm.rf: Leaderboard: 0.79295 newobs_tbl=[N=541, Y=257]; submit_filename=dupobs_Final_rf_submit OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=94.49; idseq.my=67.40; prdline.my=4.48; prdline.my.clusterid=1.99; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.79652
newobs_tbl=[N=523, Y=275]; submit_filename=dupobs_Final_rf_submit
OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=94.24; idseq.my=67.92;
prdline.my=4.33; prdline.my.clusterid=2.17; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
csmmdl: All.X.no.rnorm.rf: Leaderboard: 0.79396 newobs_tbl=[N=525, Y=273]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=111, NY=66]=177; max.Accuracy.OOB=0.8000000 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=90.30; idseq.my=67.06; prdline.my=4.40; cellular.fctr=3.57; prdline.my.clusterid=2.08;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77867 newobs_tbl=[N=564, Y=234]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=120, NY=53]=173; max.Accuracy.OOB=0.8045198 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=93.99; idseq.my=57.30; prdline.my=9.09; cellular.fctr=3.30; prdline.my.clusterid=2.35;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77152 newobs_tbl=[N=539, Y=259]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=, NY=]=; max.Accuracy.OOB=0.8011299 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=94.93; idseq.my=57.12; prdline.my=9.29; cellular.fctr=3.20; prdline.my.clusterid=2.50; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.glmnet:
fit_RMSE=???; OOB_RMSE=115.1247; new_RMSE=115.1247;
prdline.my.fctr=100.00; condition.fctrNew=88.53; D.npnct09.log=84.34
biddable=16.48; idseq.my=57.27;spdiff:
All.Interact.X.no.rnorm.rf: Leaderboard: 0.78218 newobs_tbl=[N=517, Y=281]; submit_filename=spdiff_Final_rf_submit OOB_conf_mtrx=[YN=121, NY=38]=159; max.Accuracy.OOB=0.8203390 opt.prob.threshold.OOB=0.6 biddable=100.00; startprice.diff=57.53; idseq.my=41.31; prdline.my=11.43; cellular.fctr=2.36; prdline.my.clusterid=1.82;
All.X.no.rnorm.rf:
fit_RMSE=92.19; OOB_RMSE=130.86; new_RMSE=130.86;
biddable=100.00; prdline.my.fctr=61.92; idseq.my=57.77;
condition.fctr=29.53; storage.fctr=11.22; color.fctr=6.69;
cellular.fctr=6.11
All.X.no.rnorm.rf: Leaderboard: 0.77443
newobs_tbl=[N=606, Y=192]; submit_filename=spdiff_Final_rf_submit
OOB_conf_mtrx=[YN=112, NY=28]=140; max.Accuracy.OOB=0.8418079
opt.prob.threshold.OOB=0.6
startprice.diff=100.00; biddable=96.53; idseq.my=38.10;
prdline.my=3.65; cellular.fctr=2.21; prdline.my.clusterid=0.91; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
Prediction Accuracy Enhancement Options:
- import.data chunk:
- which obs should be in fit vs. OOB (currently dirty.0 vs .1 is split 50%)
- inspect.data chunk:
- For date variables
- Appropriate factors ?
- Different / More last* features ?
- For date variables
- scrub.data chunk:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
- extract.features chunk:
- Text variables: move to date extraction chunk ???
- Mine acronyms
- Mine places
- Text variables: move to date extraction chunk ???
- Review set_global_options chunk after features are finalized
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(4) # max(length(glb_txt_vars), glb_n_cv_folds) + 1
#packageVersion("tm")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv"
glb_newdt_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv"
glb_out_pfx <- "spdiff_sp_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newobs_dataset <- TRUE # or TRUE
glb_split_entity_newobs_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_max_fitobs <- NULL # or any integer
glb_is_regression <- TRUE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE #or FALSE
glb_rsp_var_raw <- "startprice"
# for classification, the response variable has to be a factor
glb_rsp_var <- glb_rsp_var_raw #"sold.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- NULL #function(raw) {
# return(log(raw))
# ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
# }
# glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA))
glb_map_rsp_var_to_raw <- NULL #function(var) {
# return(exp(var))
# as.numeric(var) - 1
# #as.numeric(var)
# #gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# #c(FALSE, TRUE)[as.numeric(var)]
# }
# glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA)))
if ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# description = The text description of the product provided by the seller.
# biddable = Whether this is an auction (biddable=1) or a sale with a fixed price (biddable=0).
# startprice = The start price (in US Dollars) for the auction (if biddable=1) or the sale price (if biddable=0).
# condition = The condition of the product (new, used, etc.)
# cellular = Whether the iPad has cellular connectivity (cellular=1) or not (cellular=0).
# carrier = The cellular carrier for which the iPad is equipped (if cellular=1); listed as "None" if cellular=0.
# color = The color of the iPad.
# storage = The iPad's storage capacity (in gigabytes).
# productline = The name of the product being sold.
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
# Derive a numeric feature from id var
glb_id_var <- c("UniqueID")
glb_category_var <- c("prdline.my")
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features
glb_derive_lst <- NULL;
# Add logs of numerics that are not distributed normally -> do automatically ???
glb_derive_lst[["idseq.my"]] <- list(
mapfn=function(UniqueID) { return(UniqueID - 10000) }
, args=c("UniqueID"))
glb_derive_lst[["prdline.my"]] <- list(
mapfn=function(productline) { return(productline) }
, args=c("productline"))
glb_derive_lst[["startprice.log"]] <- list(
mapfn=function(startprice) { return(log(startprice)) }
, args=c("startprice"))
# glb_derive_lst[["startprice.log.zval"]] <- list(
glb_derive_lst[["descr.my"]] <- list(
mapfn=function(description) { mod_raw <- description;
# Modifications for this exercise only
# Add dictionary to stemDocument e.g. stickers stemmed to sticker ???
mod_raw <- gsub("\\.\\.", "\\. ", mod_raw);
mod_raw <- gsub("(\\w)(\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
mod_raw <- gsub("8\\.25", "825", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 10\\.SCREEN ", " 10\\. SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" actuuly ", " actual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Apple care ", " Applecare ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" ans ", " and ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" bacK!wiped ", " bacK ! wiped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" backplate", " back plate", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" bend ", " bent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("Best Buy", "BestBuy", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" black\\.Device ", " black \\. Device ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" blocks", " blocked", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" carefully ", " careful ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" conditon|condtion|conditions", " condition", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("(CONDITION|ONLY)\\.(\\w)", "\\1\\. \\2", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("(condition)(Has)", "\\1\\. \\2", mod_raw);
mod_raw <- gsub(" consist ", " consistent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" cracksNo ", " cracks No ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" DEFAULTING ", " DEFAULT ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" definitely ", " definite ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" described", " describe", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" desciption", " description", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" devices", " device", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Digi\\.", " Digitizer\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" display\\.New ", " display\\. New ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" displays", " display", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" drop ", " dropped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" effect ", " affect ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Excellant ", " Excellent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" excellently", " excellent", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" EUC ", " excellent used condition", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" feels ", " feel ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" fineiCloud ", " fine iCloud ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^Gentle ", "Gently ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" GREAT\\.SCreen ", " GREAT\\. SCreen ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" Framing ", " Frame ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("iCL0UD", "iCL0UD", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^iPad Black 3rd generation ", "iPad 3 Black ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" install\\. ", " installed\\. ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("inivisible", "invisible", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" manuals ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" book ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" mars ", " marks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" minimum", " minimal", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" MINT\\.wiped ", " MINT\\. wiped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" NEW\\!(SCREEN|ONE) ", " NEW\\! \\1 ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" new looking$", " looks new", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" newer ", " new ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" opening", " opened", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" operated", " operational", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" perfectlycord ", " perfectly cord ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" performance", " performs", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" personalized ", " personal ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" products ", " product ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Keeped ", " Kept ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" knicks ", " nicks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^READiPad ", "READ iPad ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" REFURB\\.", " REFURBISHED\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" reponding", " respond", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" rotation ", " rotate ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sales ", " Sale ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" scratchs ", " scratches ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" SCREEB ", " SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" shipped| Shipment", " ship", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("shrink wrap", "shrinkwrap", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" sides ", " side ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" skinned,", " skin,", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" spec ", " speck ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^somescratches ", "some scratches ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sticker ", " Stickers ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("SWAPPA\\.COM", "SWAPPACOM", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" T- Mobile", " TMobile", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" touchscreen ", " touch screen ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" use\\.Scratches ", " use\\. Scratches ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" verify ", " verified ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" wear\\.Device ", " wear\\. Device ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" whats ", " what's ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" WiFi\\+4G ", " WiFi \\+ 4G ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Zaag Invisible Shield", " Zaag InvisibleShield", mod_raw,
ignore.case=TRUE);
return(mod_raw) }
, args=c("description"))
# mapfn=function(startprice) { return(scale(log(startprice))) }
# , args=c("startprice"))
# mapfn=function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn=function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn=function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn=function(Week) { return(substr(Week, 1, 10)) }
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# , args=c("raw"))
# mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
# , args=c("PTS", "oppPTS"))
# # If glb_allobs_df is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glb_allobs_df)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glb_derive_lst[["<txt_var>.niso8859.log"]] <- list(
# mapfn=function(<txt_var>) { match_lst <- gregexpr("&#[[:digit:]]{3};", <txt_var>)
# match_num_vctr <- unlist(lapply(match_lst,
# function(elem) length(elem)))
# return(log(1 + match_num_vctr)) }
# , args=c("<txt_var>"))
# mapfn=function(raw) { mod_raw <- raw;
# mod_raw <- gsub("&#[[:digit:]]{3};", " ", mod_raw);
# # Modifications for this exercise only
# mod_raw <- gsub("\\bgoodIn ", "good In", mod_raw);
# return(mod_raw)
# # Create user-specified pattern vectors
# #sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
# if (txt_var %in% c("Snippet", "Abstract")) {
# txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
# as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
# glb_allobs_df[, txt_var]))
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
# glb_derive_lst[["<var1>"]] <- glb_derive_lst[["<var2>"]]
glb_derive_vars <- names(glb_derive_lst)
# tst <- "descr.my"; args_lst <- NULL; for (arg in glb_derive_lst[[tst]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glb_derive_lst[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list(); #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
glb_txt_vars <- c("descr.my")
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"glb_txt_munge_filenames_pfx <- "ebay_mytxt_"
glb_append_stop_words <- list()
# Remember to use unstemmed words
#orderBy(~ -cor.y.abs, subset(glb_feats_df, grepl("[HSA]\\.T\\.", id) & !is.na(cor.high.X)))
glb_append_stop_words[["descr.my"]] <- c(NULL
# freq = 1
,"511","825","975"
,"2nd"
,"a1314","a1430","a1432"
,"abused","across","adaptor","add","antenna","anti","anyone", "area","arizona","att"
,"beginning","bidder","bonus","boot","bound","bruises"
,"changed","changing","chrome"
,"confidence","considerable","consumer","contents","control","cream"
,"date","daughter","decent","defender","defense","degree","depicted"
,"disclaimer","distressed","divider"
,"dlxnqat9g5wt","done","dont","durable","dust","duty"
,"either","erased","ereader","essentially","every","exact"
,"faint","film","final","flickers","folding"
,"generic","genuine","glitter","goes"
,"half","handstand","hdmi","high","higher","hole","hospital"
,"impact","instead","interior"
,"jack","july"
,"keeps","kind","known"
,"last","late","let","letters","level","lifting","limited","line","lining","liquid"
,"local","long","longer","looping","loss"
,"mb292ll","mc707ll","mc916ll","mc991ll","md789ll","mf432ll","mgye2ll"
,"middle", "mind","mixed"
,"neither","none","november"
,"occasional","online","outside"
,"paperwork","period","pet","played","plug","poor","portion","pouch","price","provided"
,"ranging"
,"recently","red","reflected","repeat","required","reserve","residue","result"
,"roughly","running"
,"said","seconds","seem","semi","send","serious","setup"
,"shell","short","size","slice","smoke","smooth"
,"softer","software","somewhat","soon"
,"sparingly","sparkiling","special","speed"
,"stains","standup","status","stopped","strictly","subtle","sustained","swappacom"
,"technical","tempered","texture","thank","therefore","think","though"
,"toddler","totally","touchy","tried","typical"
,"university","unknown","untouched","upgrade"
,"valid","vary","version"
,"want","website","winning","wrapped"
,"zaag","zero", "zombie"
)
#subset(glb_allobs_df, S.T.newyorktim > 0)[, c("UniqueID", "Snippet", "S.T.newyorktim")]
#glb_txt_lst[["Snippet"]][which(glb_allobs_df$UniqueID %in% c(8394, 8317, 8339, 8350, 8307))]
glb_important_terms <- list()
# Remember to use stemmed terms
glb_filter_txt_terms <- "top" # or "sparse"
glb_top_n <- c(10)
names(glb_top_n) <- glb_txt_vars
glb_sprs_thresholds <- c(0.950) # Generates 10 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
# User-specified exclusions
glb_exclude_vars_as_features <- c("productline", "description", "startprice"
, "startprice.log", "sold"
)
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- TRUE
glb_cluster.seed <- 189 # or any integer
glb_interaction_only_features <- NULL # or ???
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "bayesglm", "glmnet", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "bayesglm", "glmnet", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=080, max=100, by=10),
#data.frame(parameter="mtry", min=08, max=10, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL #"Low.cor.X.glm"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 10.137 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/eBayiPadTrain.csv..."
## [1] "dimensions of data in ./data/eBayiPadTrain.csv: 1,861 rows x 11 cols"
## description
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## 3
## 4
## 5 Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100%
## 6
## biddable startprice condition cellular carrier color
## 1 0 159.99 Used 0 None Black
## 2 1 0.99 Used 1 Verizon Unknown
## 3 0 199.99 Used 0 None White
## 4 0 235.00 New other (see details) 0 None Unknown
## 5 0 199.99 Seller refurbished Unknown Unknown Unknown
## 6 1 175.00 Used 1 AT&T Space Gray
## storage productline sold UniqueID
## 1 16 iPad 2 0 10001
## 2 16 iPad 2 1 10002
## 3 16 iPad 4 1 10003
## 4 16 iPad mini 2 0 10004
## 5 Unknown Unknown 0 10005
## 6 32 iPad mini 2 1 10006
## description
## 65
## 283 Pristine condition, comes with a case and stylus.
## 948 \211\333\317Used Apple Ipad 16 gig 1st generation in Great working condition and 100% functional.Very little
## 1354
## 1366 Item still in complete working order, minor scratches, normal wear and tear but no damage. screen is
## 1840
## biddable startprice condition cellular carrier color
## 65 0 195.00 Used 0 None Unknown
## 283 1 20.00 Used 0 None Unknown
## 948 0 110.00 Seller refurbished 0 None Black
## 1354 0 300.00 Used 0 None White
## 1366 1 125.00 Used Unknown Unknown Unknown
## 1840 0 249.99 Used 1 Sprint Space Gray
## storage productline sold UniqueID
## 65 16 iPad mini 0 10065
## 283 64 iPad 1 0 10283
## 948 32 iPad 1 0 10948
## 1354 16 iPad Air 1 11354
## 1366 Unknown iPad 1 1 11366
## 1840 16 iPad Air 1 11840
## description
## 1856 Overall item is in good condition and is fully operational and ready to use. Comes with box and
## 1857 Used. Tested. Guaranteed to work. Physical condition grade B+ does have some light scratches and
## 1858 This item is brand new and was never used; however, the box and/or packaging has been opened.
## 1859
## 1860 This unit has minor scratches on case and several small scratches on the display. \nIt is in
## 1861 30 Day Warranty. Fully functional engraved iPad 1st Generation with signs of normal wear which
## biddable startprice condition cellular carrier
## 1856 0 89.50 Used 1 AT&T
## 1857 0 239.95 Used 0 None
## 1858 0 329.99 New other (see details) 0 None
## 1859 0 400.00 New 0 None
## 1860 0 89.00 Seller refurbished 0 None
## 1861 0 119.99 Used 1 AT&T
## color storage productline sold UniqueID
## 1856 Unknown 16 iPad 1 0 11856
## 1857 Black 32 iPad 4 1 11857
## 1858 Space Gray 16 iPad Air 0 11858
## 1859 Gold 16 iPad mini 3 0 11859
## 1860 Black 64 iPad 1 1 11860
## 1861 Black 64 iPad 1 0 11861
## 'data.frame': 1861 obs. of 11 variables:
## $ description: chr "iPad is in 8.5+ out of 10 cosmetic condition!" "Previously used, please read description. May show signs of use such as scratches to the screen and " "" "" ...
## $ biddable : int 0 1 0 0 0 1 1 0 1 1 ...
## $ startprice : num 159.99 0.99 199.99 235 199.99 ...
## $ condition : chr "Used" "Used" "Used" "New other (see details)" ...
## $ cellular : chr "0" "1" "0" "0" ...
## $ carrier : chr "None" "Verizon" "None" "None" ...
## $ color : chr "Black" "Unknown" "White" "Unknown" ...
## $ storage : chr "16" "16" "16" "16" ...
## $ productline: chr "iPad 2" "iPad 2" "iPad 4" "iPad mini 2" ...
## $ sold : int 0 1 1 0 0 1 1 0 1 1 ...
## $ UniqueID : int 10001 10002 10003 10004 10005 10006 10007 10008 10009 10010 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- read.delim("data/hygiene.txt", header=TRUE, fill=TRUE, sep="\t",
# fileEncoding='iso-8859-1')
# glb_trnobs_df <- read.table("data/hygiene.dat.labels", col.names=c("dirty"),
# na.strings="[none]")
# glb_trnobs_df$review <- readLines("data/hygiene.dat", n =-1)
# comment(glb_trnobs_df) <- "glb_trnobs_df"
# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newobs_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newobs_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newobs_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/eBayiPadTest.csv..."
## [1] "dimensions of data in ./data/eBayiPadTest.csv: 798 rows x 10 cols"
## description
## 1 like new
## 2 Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though
## 3 This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer.
## 4
## 5 Grade A condition means that the Ipad is 100% working condition. Cosmetically 8/9 out of 10 - Will
## 6 Brand new factory sealed iPad in an OPEN BOX...THE BOX ITSELF IS HEAVILY DISTRESSED(see
## biddable startprice condition cellular carrier color
## 1 0 105.00 Used 1 AT&T Unknown
## 2 0 195.00 Used 0 None Unknown
## 3 0 219.99 Used 0 None Unknown
## 4 1 100.00 Used 0 None Unknown
## 5 0 210.99 Manufacturer refurbished 0 None Black
## 6 0 514.95 New other (see details) 0 None Gold
## storage productline UniqueID
## 1 32 iPad 1 11862
## 2 16 iPad mini 2 11863
## 3 64 iPad 3 11864
## 4 16 iPad mini 11865
## 5 32 iPad 3 11866
## 6 64 iPad Air 2 11867
## description
## 1 like new
## 142 iPad mini 1st gen wi-fi 16gb is in perfect working order.
## 309 In excellent condition. Minor scratches on the back. Screen in mint condition. Comes in original
## 312 iPad is in Great condition, the screen is in great condition showing only a few minor scratches, the
## 320 Good condition and fully functional
## 369
## biddable startprice condition cellular carrier color storage
## 1 0 105.00 Used 1 AT&T Unknown 32
## 142 1 0.99 Used 0 None Unknown 16
## 309 0 200.00 Used 1 AT&T Black 32
## 312 1 0.99 Used 0 None Unknown 16
## 320 1 60.00 Used 0 None White 16
## 369 1 197.97 Used 0 None Unknown 64
## productline UniqueID
## 1 iPad 1 11862
## 142 iPad mini 12003
## 309 iPad 3 12170
## 312 iPad mini 2 12173
## 320 iPad 1 12181
## 369 iPad mini 3 12230
## description
## 793 Crack on digitizer near top. Top line of digitizer does not respond to touch. Other than that, all
## 794
## 795
## 796
## 797
## 798 Slightly Used. Includes everything you need plus a nice leather case!\nThere is a slice mark on the
## biddable startprice condition cellular carrier color
## 793 0 104.00 For parts or not working 1 Unknown Black
## 794 0 95.00 Used 1 AT&T Unknown
## 795 1 199.99 Manufacturer refurbished 0 None White
## 796 0 149.99 Used 0 None Unknown
## 797 0 7.99 New Unknown Unknown Unknown
## 798 0 139.00 Used 1 Unknown Black
## storage productline UniqueID
## 793 16 iPad 2 12654
## 794 64 iPad 1 12655
## 795 16 iPad 4 12656
## 796 16 iPad 2 12657
## 797 Unknown iPad 3 12658
## 798 32 Unknown 12659
## 'data.frame': 798 obs. of 10 variables:
## $ description: chr "like new" "Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though " "This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer." "" ...
## $ biddable : int 0 0 0 1 0 0 0 0 0 1 ...
## $ startprice : num 105 195 220 100 211 ...
## $ condition : chr "Used" "Used" "Used" "Used" ...
## $ cellular : chr "1" "0" "0" "0" ...
## $ carrier : chr "AT&T" "None" "None" "None" ...
## $ color : chr "Unknown" "Unknown" "Unknown" "Unknown" ...
## $ storage : chr "32" "16" "64" "16" ...
## $ productline: chr "iPad 1" "iPad mini 2" "iPad 3" "iPad mini" ...
## $ UniqueID : int 11862 11863 11864 11865 11866 11867 11868 11869 11870 11871 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Combine trnent & newobs into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Train"))
glb_newobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Test"))
glb_id_var <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_var)
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
# For Tableau
write.csv(glb_allobs_df, "data/eBayiPadAll.csv", row.names=FALSE)
#stop(here")
glb_drop_obs <- c(
11234, #sold=0; 2 other dups(10306, 11503) are sold=1
11844, #sold=0; 3 other dups(11721, 11738, 11812) are sold=1
NULL)
glb_allobs_df <- glb_allobs_df[!glb_allobs_df[, glb_id_var] %in% glb_drop_obs, ]
# Make any data corrections here
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "cellular"] <- "1"
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "carrier"] <- "T-Mobile"
# Check for duplicates by all features
require(gdata)## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size#print(names(glb_allobs_df))
dup_allobs_df <- glb_allobs_df[duplicated2(subset(glb_allobs_df,
select=-c(UniqueID, sold, .src))), ]
dup_allobs_df <- orderBy(~productline+description+startprice+biddable, dup_allobs_df)
print(sprintf("Found %d duplicates by all features:", nrow(dup_allobs_df)))## [1] "Found 304 duplicates by all features:"myprint_df(dup_allobs_df)## description biddable startprice condition cellular
## 1711 1 0.99 For parts or not working Unknown
## 2608 1 0.99 For parts or not working Unknown
## 293 1 5.00 Used Unknown
## 478 1 5.00 Used Unknown
## 385 0 15.00 Used 0
## 390 0 15.00 Used 0
## carrier color storage productline sold UniqueID .src
## 1711 Unknown Unknown 16 Unknown 1 11711 Train
## 2608 Unknown Unknown 16 Unknown NA 12608 Test
## 293 Unknown White 16 Unknown 1 10293 Train
## 478 Unknown White 16 Unknown 1 10478 Train
## 385 None Black 16 Unknown 0 10385 Train
## 390 None Black 16 Unknown 0 10390 Train
## description biddable startprice condition cellular
## 1956 1 0.99 Used 0
## 828 1 249.97 Manufacturer refurbished 1
## 3 0 199.99 Used 0
## 1649 0 209.00 For parts or not working Unknown
## 2111 1 200.00 Used 0
## 172 0 269.00 Used 0
## carrier color storage productline sold UniqueID .src
## 1956 None Unknown 16 iPad 2 NA 11956 Test
## 828 Unknown Black 64 iPad 2 0 10828 Train
## 3 None White 16 iPad 4 1 10003 Train
## 1649 Unknown Unknown 16 iPad Air 0 11649 Train
## 2111 None Space Gray 64 iPad mini 2 NA 12111 Test
## 172 None Unknown 32 iPad mini 2 0 10172 Train
## description biddable startprice condition cellular carrier color
## 8 0 329.99 New 0 None White
## 660 0 329.99 New 0 None White
## 319 0 345.00 New 0 None Gold
## 1886 0 345.00 New 0 None Gold
## 1363 0 498.88 New 1 Verizon Gold
## 1394 0 498.88 New 1 Verizon Gold
## storage productline sold UniqueID .src
## 8 16 iPad mini 3 0 10008 Train
## 660 16 iPad mini 3 0 10660 Train
## 319 16 iPad mini 3 1 10319 Train
## 1886 16 iPad mini 3 NA 11886 Test
## 1363 16 iPad mini 3 0 11363 Train
## 1394 16 iPad mini 3 0 11394 Train# print(dup_allobs_df[, c(glb_id_var, glb_rsp_var_raw,
# "description", "startprice", "biddable")])
# write.csv(dup_allobs_df[, c("UniqueID"), FALSE], "ebayipads_dups.csv", row.names=FALSE)
dupobs_df <- tidyr::unite(dup_allobs_df, "allfeats", -c(sold, UniqueID, .src), sep="#")
# dupobs_df <- dplyr::group_by(dupobs_df, allfeats)
# dupobs_df <- dupobs_df[, "UniqueID", FALSE]
# dupobs_df <- ungroup(dupobs_df)
#
# dupobs_df$.rownames <- row.names(dupobs_df)
grpobs_df <- data.frame(allfeats=unique(dupobs_df[, "allfeats"]))
grpobs_df$.grpid <- row.names(grpobs_df)
dupobs_df <- merge(dupobs_df, grpobs_df)
# dupobs_tbl <- table(dupobs_df$.grpid)
# print(max(dupobs_tbl))
# print(dupobs_tbl[which.max(dupobs_tbl)])
# print(dupobs_df[dupobs_df$.grpid == names(dupobs_tbl[which.max(dupobs_tbl)]), ])
# print(dupobs_df[dupobs_df$.grpid == 106, ])
# for (grpid in c(9, 17, 31, 36, 53))
# print(dupobs_df[dupobs_df$.grpid == grpid, ])
dupgrps_df <- as.data.frame(table(dupobs_df$.grpid, dupobs_df$sold, useNA="ifany"))
names(dupgrps_df)[c(1,2)] <- c(".grpid", "sold")
dupgrps_df$.grpid <- as.numeric(as.character(dupgrps_df$.grpid))
dupgrps_df <- tidyr::spread(dupgrps_df, sold, Freq)
names(dupgrps_df)[-1] <- paste("sold", names(dupgrps_df)[-1], sep=".")
dupgrps_df$.freq <- sapply(1:nrow(dupgrps_df), function(row) sum(dupgrps_df[row, -1]))
myprint_df(orderBy(~-.freq, dupgrps_df))## .grpid sold.0 sold.1 sold.NA .freq
## 40 40 0 6 3 9
## 106 106 0 4 1 5
## 9 9 0 1 3 4
## 17 17 0 3 1 4
## 36 36 0 3 1 4
## 53 53 0 2 2 4
## .grpid sold.0 sold.1 sold.NA .freq
## 10 10 0 2 0 2
## 42 42 0 1 1 2
## 57 57 1 0 1 2
## 66 66 1 0 1 2
## 91 91 0 1 1 2
## 101 101 0 1 1 2
## .grpid sold.0 sold.1 sold.NA .freq
## 130 130 1 0 1 2
## 131 131 1 1 0 2
## 132 132 0 1 1 2
## 133 133 2 0 0 2
## 134 134 0 1 1 2
## 135 135 2 0 0 2print("sold Conflicts:")## [1] "sold Conflicts:"print(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 4 4 1 1 0 2
## 22 22 1 1 0 2
## 23 23 1 1 0 2
## 74 74 1 1 0 2
## 83 83 1 1 0 2
## 84 84 1 1 0 2
## 95 95 1 1 0 2
## 102 102 1 1 0 2
## 109 109 1 1 0 2
## 111 111 1 1 0 2
## 122 122 1 1 0 2
## 131 131 1 1 0 2#dupobs_df[dupobs_df$.grpid == 4, ]
if (nrow(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0) & (sold.0 != sold.1))) > 0)
stop("Duplicate conflicts are resolvable")
print("Test & Train Groups:")## [1] "Test & Train Groups:"print(subset(dupgrps_df, (sold.NA > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 1 1 0 1 1 2
## 5 5 1 0 1 2
## 7 7 0 0 2 2
## 8 8 1 0 1 2
## 9 9 0 1 3 4
## 12 12 0 0 2 2
## 14 14 0 1 1 2
## 15 15 0 0 2 2
## 17 17 0 3 1 4
## 18 18 0 2 1 3
## 19 19 0 2 1 3
## 24 24 0 2 1 3
## 26 26 1 0 1 2
## 28 28 1 0 1 2
## 30 30 0 1 1 2
## 32 32 0 0 2 2
## 33 33 0 1 1 2
## 35 35 0 2 1 3
## 36 36 0 3 1 4
## 37 37 0 0 2 2
## 38 38 0 1 1 2
## 40 40 0 6 3 9
## 41 41 0 0 2 2
## 42 42 0 1 1 2
## 43 43 0 1 1 2
## 44 44 0 2 1 3
## 47 47 0 1 1 2
## 48 48 0 0 2 2
## 49 49 0 1 2 3
## 51 51 0 1 1 2
## 53 53 0 2 2 4
## 54 54 0 1 1 2
## 55 55 1 0 2 3
## 56 56 1 0 1 2
## 57 57 1 0 1 2
## 58 58 0 0 2 2
## 59 59 1 0 1 2
## 60 60 1 0 1 2
## 63 63 0 1 1 2
## 66 66 1 0 1 2
## 67 67 1 0 1 2
## 68 68 0 0 2 2
## 69 69 1 0 1 2
## 73 73 0 1 1 2
## 76 76 0 2 1 3
## 86 86 0 0 2 2
## 87 87 1 0 1 2
## 89 89 1 0 1 2
## 90 90 0 0 2 2
## 91 91 0 1 1 2
## 93 93 0 1 1 2
## 94 94 1 0 1 2
## 99 99 0 1 1 2
## 101 101 0 1 1 2
## 103 103 0 1 1 2
## 104 104 1 0 1 2
## 106 106 0 4 1 5
## 107 107 0 1 1 2
## 108 108 0 1 1 2
## 112 112 1 0 1 2
## 114 114 0 1 1 2
## 115 115 0 1 1 2
## 116 116 1 0 1 2
## 117 117 0 2 1 3
## 118 118 0 1 1 2
## 121 121 1 0 1 2
## 124 124 1 0 1 2
## 128 128 0 1 1 2
## 130 130 1 0 1 2
## 132 132 0 1 1 2
## 134 134 0 1 1 2glb_allobs_df <- merge(glb_allobs_df, dupobs_df[, c(glb_id_var, ".grpid")],
by=glb_id_var, all.x=TRUE)
glb_exclude_vars_as_features <- c(".grpid", glb_exclude_vars_as_features)
# spd_allobs_df <- read.csv(paste0(glb_out_pfx, "sp_predict.csv"))
# if (nrow(spd_allobs_df) != nrow(glb_allobs_df))
# stop("mismatches between spd_allobs_df & glb_allobs_df")
# mrg_allobs_df <- merge(glb_allobs_df, spd_allobs_df)
# if (nrow(mrg_allobs_df) != nrow(glb_allobs_df))
# stop("mismatches between mrg_allobs_df & glb_allobs_df")
# mrg_allobs_df$startprice.diff <- mrg_allobs_df$startprice -
# mrg_allobs_df$startprice.predict.
# print(myplot_scatter(mrg_allobs_df, "startprice", "startprice.diff",
# colorcol_name = "biddable"))
# print(myplot_histogram(mrg_allobs_df, "startprice.diff",
# fill_col_name = "biddable"))
# glb_allobs_df <- mrg_allobs_df
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
# "startprice.log", "startprice.predict.")
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# Only for _sp
print(table(glb_allobs_df$sold, glb_allobs_df$.src, useNA = "ifany"))##
## Test Train
## 0 0 999
## 1 0 860
## <NA> 798 0print(table(glb_allobs_df$sold, glb_allobs_df$biddable, glb_allobs_df$.src,
useNA = "ifany"))## , , = Test
##
##
## 0 1
## 0 0 0
## 1 0 0
## <NA> 422 376
##
## , , = Train
##
##
## 0 1
## 0 802 197
## 1 220 640
## <NA> 0 0glb_allobs_df$.src <- "Test"
glb_allobs_df[!is.na(glb_allobs_df$sold) & (glb_allobs_df$sold == 1), ".src"] <- "Train"
print(table(glb_allobs_df$sold, glb_allobs_df$.src, useNA = "ifany"))##
## Test Train
## 0 999 0
## 1 0 860
## <NA> 798 0print(table(glb_allobs_df$sold, glb_allobs_df$biddable, glb_allobs_df$.src,
useNA = "ifany"))## , , = Test
##
##
## 0 1
## 0 802 197
## 1 0 0
## <NA> 422 376
##
## , , = Train
##
##
## 0 1
## 0 0 0
## 1 220 640
## <NA> 0 0###
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 10.137 16.272 6.136
## 2 inspect.data 2 0 16.273 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df)
}
glb_chk_data()## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1444 999
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid
## 0 NA# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}
# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
for (feat in feats_vctr) {
print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:gdata':
##
## combine, first, last
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, "<descriptor>")
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glb_exclude_vars_as_features)))## [1] "feat: biddable"## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## [1] "feat: .rnorm"## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 16.273 18.69 2.417
## 3 scrub.data 2 1 18.691 NA NAStep 2.1: scrub data
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1444 999
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid
## 0 NAfindOffendingCharacter <- function(x, maxStringLength=256){
print(x)
for (c in 1:maxStringLength){
offendingChar <- substr(x,c,c)
#print(offendingChar) #uncomment if you want the indiv characters printed
#the next character is the offending multibyte Character
}
}
# string_vector <- c("test", "Se\x96ora", "works fine")
# lapply(string_vector, findOffendingCharacter)
# lapply(glb_allobs_df$description[29], findOffendingCharacter)
sel_obs <- function(vars_lst, ignore.case=TRUE, perl=FALSE) {
tmp_df <- glb_allobs_df
# Does not work for Popular == NAs ???
# if (!is.null(Popular)) {
# if (is.na(Popular))
# tmp_df <- tmp_df[is.na(tmp_df$Popular), ] else
# tmp_df <- tmp_df[tmp_df$Popular == Popular, ]
# }
# if (!is.null(NewsDesk))
# tmp_df <- tmp_df[tmp_df$NewsDesk == NewsDesk, ]
for (var in names(vars_lst)) {
if (grepl(".contains", var))
tmp_df <- tmp_df[grep(vars_lst[var],
tmp_df[, unlist(strsplit(var, ".contains"))],
ignore.case=ignore.case, perl=perl), ]
else
tmp_df <- tmp_df[tmp_df[, var] == vars_lst[var], ]
}
return(glb_allobs_df[, glb_id_var] %in% tmp_df[, glb_id_var])
}
#print(glb_allobs_df[sel_obs(list(description.contains="mini(?!m)"), perl=TRUE), "description"])
dsp_obs <- function(..., cols=c(NULL), all=FALSE) {
tmp_df <- glb_allobs_df[sel_obs(...),
c(glb_id_var, glb_rsp_var, glb_category_var, glb_txt_vars, cols),
FALSE]
if(all) { print(tmp_df) } else { myprint_df(tmp_df) }
}
#dsp_obs(list(description.contains="mini(?!m)"), perl=TRUE)
#dsp_obs(Popular=1, NewsDesk="", SectionName="", Headline.contains="Boehner")
# dsp_obs(Popular=1, NewsDesk="", SectionName="")
# dsp_obs(Popular=NA, NewsDesk="", SectionName="")
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "dummy" = "dummy"
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName"))
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1593 0 0 0 0 0
## 1 288 0 4 36 28 172 196
## Unknown 4 4 2 0 0 330 0# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("AT&T", "Other")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("AT&T", "Other")),
"cellular"] <- "1"
# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("None")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("None")),
"cellular"] <- "0"
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1597 0 0 0 0 0
## 1 292 0 6 36 28 172 196
## Unknown 0 0 0 0 0 330 0Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 scrub.data 2 1 18.691 19.371 0.68
## 4 transform.data 2 2 19.371 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_assign_vars)
}
### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
print(sprintf("Creating new feature: %s...", new_feat))
args_lst <- NULL
for (arg in glb_derive_lst[[new_feat]]$args)
args_lst[[arg]] <- glb_allobs_df[, arg]
glb_allobs_df[, new_feat] <- do.call(glb_derive_lst[[new_feat]]$mapfn, args_lst)
}## [1] "Creating new feature: idseq.my..."
## [1] "Creating new feature: prdline.my..."
## [1] "Creating new feature: startprice.log..."
## [1] "Creating new feature: descr.my..."#stop(here")
#hex_vctr <- c("\n", "\211", "\235", "\317", "\333")
hex_regex <- paste0(c("\n", "\211", "\235", "\317", "\333"), collapse="|")
for (obs_id in c(10178, 10948, 11514, 11904, 12157, 12210, 12659)) {
# tmp_str <- unlist(strsplit(glb_allobs_df[row_pos, "descr.my"], ""))
# glb_allobs_df[row_pos, "descr.my"] <- paste0(tmp_str[!tmp_str %in% hex_vctr],
# collapse="")
row_pos <- which(glb_allobs_df$UniqueID == obs_id)
glb_allobs_df[row_pos, "descr.my"] <-
gsub(hex_regex, " ", glb_allobs_df[row_pos, "descr.my"])
}Step 2.2: transform data
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 4 transform.data 2 2 19.371 20.088 0.717
## 5 extract.features 3 0 20.089 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor bgn end elapsed
## 1 extract.features_bgn 1 0 20.096 NA NA# Options:
# Select Tf, log(1 + Tf), Tf-IDF or BM25Tf-IDf
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
#stop(here"); sav_allobs_df <- glb_allobs_df #; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
for (sfx in c("", ".POSIX"))
glb_exclude_vars_as_features <-
union(glb_exclude_vars_as_features,
paste(glb_date_vars, sfx, sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last2 <- as.numeric(merge(z-lag(z, -2), b, all=TRUE)); last2[is.na(last2)] <- 0
glb_allobs_df[, paste0(feat, ".last2.log")] <- log(1 + last2)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last2.log")] > 0, ],
ycol_names=paste0(feat, ".last2.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}
rm(last1, last10, last100)## Warning in rm(last1, last10, last100): object 'last1' not found## Warning in rm(last1, last10, last100): object 'last10' not found## Warning in rm(last1, last10, last100): object 'last100' not found# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor bgn end
## 1 extract.features_bgn 1 0 20.096 20.109
## 2 extract.features_factorize.str.vars 2 0 20.109 NA
## elapsed
## 1 0.013
## 2 NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## description condition cellular carrier color
## "description" "condition" "cellular" "carrier" "color"
## storage productline .src .grpid prdline.my
## "storage" "productline" ".src" ".grpid" "prdline.my"
## descr.my
## "descr.my"if (length(str_vars <- setdiff(str_vars,
c(glb_exclude_vars_as_features, glb_txt_vars))) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <-
relevel(factor(glb_allobs_df[, var]),
names(which.max(table(glb_allobs_df[, var], useNA = "ifany"))))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}## Warning: Creating factors of string variable: condition: # of unique
## values: 6## Warning: Creating factors of string variable: cellular: # of unique values:
## 3## Warning: Creating factors of string variable: carrier: # of unique values:
## 7## Warning: Creating factors of string variable: color: # of unique values: 5## Warning: Creating factors of string variable: storage: # of unique values:
## 5## Warning: Creating factors of string variable: prdline.my: # of unique
## values: 12if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(rex_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(rex_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
# match_lst <- gregexpr("\\bok(?!ay)", txt_vctr[746], ignore.case = FALSE, perl=TRUE); print(match_lst)
dsp_pattern <- function(rex_str, ignore.case=TRUE, print.all=TRUE) {
match_lst <- gregexpr(rex_str, txt_vctr, ignore.case = ignore.case, perl=TRUE)
match_lst <- regmatches(txt_vctr, match_lst)
match_df <- data.frame(matches=sapply(match_lst,
function (elems) paste(elems, collapse="#")))
match_df <- subset(match_df, matches != "")
if (print.all)
print(match_df)
return(match_df)
}
dsp_matches <- function(rex_str, ix) {
print(match_pos <- gregexpr(rex_str, txt_vctr[ix], perl=TRUE))
print(str_sub(txt_vctr[ix], (match_pos[[1]] / 100) * 99 + 0,
(match_pos[[1]] / 100) * 100 + 100))
}
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end],
glb_txt_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_lst; all.equal(sav_txt_lst, glb_txt_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_lst[["Headline"]][1:4200])
#chk.equal( 1, 100)
#dsp.equal(86, 90)
txt_map_filename <- paste0(glb_txt_munge_filenames_pfx, "map.csv")
if (!file.exists(txt_map_filename))
stop(txt_map_filename, " not found!")
glb_txt_map_df <- read.csv(txt_map_filename, comment.char="#", strip.white=TRUE)
glb_txt_lst <- list();
print(sprintf("Building glb_txt_lst..."))
glb_txt_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[3, "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining OK in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "(?<!(BO|HO|LO))OK(?!(E\\!|ED|IE|IN|S ))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "Ok(?!(a\\.|ay|in|ra|um))", ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "(?<!( b| B| c| C| g| G| j| M| p| P| w| W| r| Z|\\(b|ar|bo|Bo|co|Co|Ew|gk|go|ho|ig|jo|kb|ke|Ke|ki|lo|Lo|mo|mt|no|No|po|ra|ro|sm|Sm|Sp|to|To))ok(?!(ay|bo|e |e\\)|e,|e\\.|eb|ed|el|en|er|es|ey|i |ie|in|it|ka|ke|ki|ly|on|oy|ra|st|u |uc|uy|yl|yo))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
}
# txt_vctr <- glb_txt_lst[[glb_txt_vars[1]]]
# print(chk_pattern_freq(rex_str <- "(?<!( b| c| C| p|\\(b|bo|co|lo|Lo|Sp|to|To))ok(?!(ay|e |e\\)|e,|e\\.|ed|el|en|es|ey|ie|in|on|ra))", ignore.case=FALSE))
# print(chk_pattern_freq(rex_str <- "ok(?!(ay|el|on|ra))", ignore.case=FALSE))
# dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
# dsp_matches(rex_str, ix=8)
# substr(txt_vctr[86], 5613, 5620)
# substr(glb_allobs_df[301, "review"], 550, 650)
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "([[:upper:]]\\.( *)){2,}", ignore.case=FALSE))
# Check for names
print(subset(chk_pattern_freq(rex_str <- "(([[:upper:]]+)\\.( *)){1}",
ignore.case=FALSE),
.n > 1))
# dsp_pattern(rex_str="(OK\\.( *)){1}", ignore.case=FALSE)
# dsp_matches(rex_str="(OK\\.( *)){1}", ix=557)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)(\\B)", ix=461)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)", ix=461)
#print(str_sub(txt_vctr[676], 10100, 10200))
#print(str_sub(txt_vctr[74], 1, -1))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
if (nrow(filtered_df <- subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))) > 0)
print(orderBy(~ -.n +pattern, filtered_df))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE,
preserve_intra_word_dashes=TRUE, lazy=TRUE)
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full, full_Tf_df)
cmpnd_Tf_df <- full_Tf_df[grep("-", full_Tf_df$term, value=TRUE) ,]
txt_compound_filename <- paste0(glb_txt_munge_filenames_pfx, "compound.csv")
if (!file.exists(txt_compound_filename))
stop(txt_compound_filename, " not found!")
filter_df <- read.csv(txt_compound_filename, comment.char="#", strip.white=TRUE)
cmpnd_Tf_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "term"], ignore.case=TRUE)
cmpnd_Tf_df <- subset(cmpnd_Tf_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_Tf_df): %d", nrow(cmpnd_Tf_df)))
myprint_df(cmpnd_Tf_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
# find_cmpnd_wrds(txt_vctr)
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
get_DTM_terms <- function(DTM) {
TfIdf_mtrx <- as.matrix(DTM)
rownames(TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
TfIdf_vctr <- colSums(TfIdf_mtrx)
names(TfIdf_vctr) <- dimnames(DTM)[[2]]
TfIdf_df <- as.data.frame(TfIdf_vctr)
names(TfIdf_df) <- "TfIdf"
TfIdf_df$term <- rownames(TfIdf_df)
TfIdf_df$freq <- colSums(TfIdf_mtrx != 0)
TfIdf_df$pos <- 1:nrow(TfIdf_df)
return(TfIdf_df <- orderBy(~ -TfIdf, TfIdf_df))
}
get_corpus_terms <- function(txt_corpus) {
TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
return(TfIdf_df <- get_DTM_terms(TfIdf_DTM))
}
#stop(here")
glb_corpus_lst <- list()
print(sprintf("Building glb_corpus_lst..."))
glb_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_lst[[txt_var]]))
#tolower Not needed as of version 0.6.2 ?
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=FALSE)
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=FALSE) #nuppr
# removePunctuation does not replace with whitespace. Use a custom transformer ???
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english")), lazy=TRUE) #nstopwrds
#print("StoppedWords:"); stopped_words_TfIdf_df <- inspect_terms(txt_corpus)
#stopped_words_TfIdf_df[grepl("cond", stopped_words_TfIdf_df$term, ignore.case=TRUE), ]
#txt_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#which(txt_X_mtrx[, 211] > 0)
#glb_allobs_df[which(txt_X_mtrx[, 211] > 0), glb_txt_vars]
#txt_X_mtrx[2159, txt_X_mtrx[2159, ] > 0]
# txt_corpus <- tm_map(txt_corpus, stemDocument, "english", lazy=TRUE) #Done below
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_TfIdf_df <- inspect_terms(txt_corpus)
#stemmed_words_TfIdf_df[grepl("cond", stemmed_words_TfIdf_df$term, ignore.case=TRUE), ]
#stm_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#glb_allobs_df[which((stm_X_mtrx[, 180] > 0) | (stm_X_mtrx[, 181] > 0)), glb_txt_vars]
#glb_allobs_df[which((stm_X_mtrx[, 181] > 0)), glb_txt_vars]
# glb_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_corpus_lst) <- glb_txt_vars
#stop(here")
glb_post_stop_words_terms_df_lst <- list();
glb_post_stop_words_TfIdf_mtrx_lst <- list();
glb_post_stem_words_terms_df_lst <- list();
glb_post_stem_words_TfIdf_mtrx_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf(" Top_n stop TfIDf terms for %s:", txt_var))
# This impacts stemming probably due to lazy parameter
print(myprint_df(full_TfIdf_df <- get_corpus_terms(glb_corpus_lst[[txt_var]]),
glb_top_n[[txt_var]]))
glb_post_stop_words_terms_df_lst[[txt_var]] <- full_TfIdf_df
TfIdf_stop_mtrx <- as.matrix(DocumentTermMatrix(glb_corpus_lst[[txt_var]],
control=list(weighting=weightTfIdf)))
rownames(TfIdf_stop_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stop_words_TfIdf_mtrx_lst[[txt_var]] <- TfIdf_stop_mtrx
tmp_allobs_df <- glb_allobs_df[, c(glb_id_var, glb_rsp_var)]
tmp_allobs_df$terms.n.post.stop <- rowSums(TfIdf_stop_mtrx > 0)
tmp_allobs_df$terms.n.post.stop.log <- log(1 + tmp_allobs_df$terms.n.post.stop)
tmp_allobs_df$TfIdf.sum.post.stop <- rowSums(TfIdf_stop_mtrx)
print(sprintf(" Top_n stem TfIDf terms for %s:", txt_var))
glb_corpus_lst[[txt_var]] <- tm_map(glb_corpus_lst[[txt_var]], stemDocument,
"english", lazy=TRUE) #Features ???
print(myprint_df(full_TfIdf_df <- get_corpus_terms(glb_corpus_lst[[txt_var]]),
glb_top_n[[txt_var]]))
glb_post_stem_words_terms_df_lst[[txt_var]] <- full_TfIdf_df
TfIdf_stem_mtrx <- as.matrix(DocumentTermMatrix(glb_corpus_lst[[txt_var]],
control=list(weighting=weightTfIdf)))
rownames(TfIdf_stem_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stem_words_TfIdf_mtrx_lst[[txt_var]] <- TfIdf_stem_mtrx
tmp_allobs_df$terms.n.post.stem <- rowSums(TfIdf_stem_mtrx > 0)
tmp_allobs_df$terms.n.post.stem.log <- log(1 + tmp_allobs_df$terms.n.post.stem)
tmp_allobs_df$TfIdf.sum.post.stem <- rowSums(TfIdf_stem_mtrx)
tmp_allobs_df$terms.n.stem.stop.Ratio <-
1.0 * tmp_allobs_df$terms.n.post.stem / tmp_allobs_df$terms.n.post.stop
tmp_allobs_df[is.nan(tmp_allobs_df$terms.n.stem.stop.Ratio),
"terms.n.stem.stop.Ratio"] <- 1.0
tmp_allobs_df$TfIdf.sum.stem.stop.Ratio <-
1.0 * tmp_allobs_df$TfIdf.sum.post.stem / tmp_allobs_df$TfIdf.sum.post.stop
tmp_allobs_df[is.nan(tmp_allobs_df$TfIdf.sum.stem.stop.Ratio),
"TfIdf.sum.stem.stop.Ratio"] <- 1.0
tmp_trnobs_df <- tmp_allobs_df[!is.na(tmp_allobs_df[, glb_rsp_var]), ]
print(cor(as.matrix(tmp_trnobs_df[, -c(1, 2)]),
as.numeric(tmp_trnobs_df[, glb_rsp_var])))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
tmp_allobs_df <- tmp_allobs_df[, -c(1, 2)]
names(tmp_allobs_df) <- paste(paste0(txt_var_pfx, "."), names(tmp_allobs_df),
sep="")
glb_allobs_df <- cbind(glb_allobs_df, tmp_allobs_df)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
paste(txt_var_pfx, c("terms.n.post.stop", "terms.n.post.stem")))
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
#stop(here")
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting TfIDf terms for %s...", txt_var))
txt_corpus <- glb_corpus_lst[[txt_var]]
# full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
# control=list(weighting=weightTf))
full_TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
sprs_TfIdf_DTM <- removeSparseTerms(full_TfIdf_DTM,
glb_sprs_thresholds[txt_var])
# glb_full_DTM_lst[[txt_var]] <- full_Tf_DTM
# glb_sprs_DTM_lst[[txt_var]] <- sprs_Tf_DTM
glb_full_DTM_lst[[txt_var]] <- full_TfIdf_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_TfIdf_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
require(reshape2)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting TfIDf terms for %s...", txt_var))
full_TfIdf_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_TfIdf_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_TfIdf_DTM)
full_TfIdf_df <- get_DTM_terms(full_TfIdf_DTM)
full_TfIdf_df <- full_TfIdf_df[, c(2, 1, 3, 4)]
col_names <- names(full_TfIdf_df)
col_names[2:length(col_names)] <-
paste(col_names[2:length(col_names)], ".full", sep="")
names(full_TfIdf_df) <- col_names
# full_TfIdf_mtrx <- as.matrix(full_TfIdf_DTM)
# rownames(full_TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
# full_TfIdf_vctr <- colSums(full_TfIdf_mtrx)
# names(full_TfIdf_vctr) <- dimnames(full_TfIdf_DTM)[[2]]
# full_TfIdf_df <- as.data.frame(full_TfIdf_vctr)
# names(full_TfIdf_df) <- "TfIdf.full"
# full_TfIdf_df$term <- rownames(full_TfIdf_df)
# full_TfIdf_df$freq.full <- colSums(full_TfIdf_mtrx != 0)
# full_TfIdf_df <- orderBy(~ -TfIdf.full, full_TfIdf_df)
print(" Sparse TermMatrix:"); print(sprs_TfIdf_DTM)
sprs_TfIdf_df <- get_DTM_terms(sprs_TfIdf_DTM)
sprs_TfIdf_df <- sprs_TfIdf_df[, c(2, 1, 3, 4)]
col_names <- names(sprs_TfIdf_df)
col_names[2:length(col_names)] <-
paste(col_names[2:length(col_names)], ".sprs", sep="")
names(sprs_TfIdf_df) <- col_names
# sprs_TfIdf_vctr <- colSums(as.matrix(sprs_TfIdf_DTM))
# names(sprs_TfIdf_vctr) <- dimnames(sprs_TfIdf_DTM)[[2]]
# sprs_TfIdf_df <- as.data.frame(sprs_TfIdf_vctr)
# names(sprs_TfIdf_df) <- "TfIdf.sprs"
# sprs_TfIdf_df$term <- rownames(sprs_TfIdf_df)
# sprs_TfIdf_df$freq.sprs <- colSums(as.matrix(sprs_TfIdf_DTM) != 0)
# sprs_TfIdf_df <- orderBy(~ -TfIdf.sprs, sprs_TfIdf_df)
terms_TfIdf_df <- merge(full_TfIdf_df, sprs_TfIdf_df, all.x=TRUE)
terms_TfIdf_df$in.sprs <- !is.na(terms_TfIdf_df$freq.sprs)
plt_TfIdf_df <- subset(terms_TfIdf_df,
TfIdf.full >= min(terms_TfIdf_df$TfIdf.sprs, na.rm=TRUE))
plt_TfIdf_df$label <- ""
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "label"] <-
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"]
# glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
# plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_TfIdf_df <- orderBy(~ -value, melt(terms_TfIdf_df, id.var="term"))
print(ggplot(melt_TfIdf_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, !is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(melt_TfIdf_df, "term", "value",
colorcol_name="variable"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(head(melt_TfIdf_df, 10), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# sav_sprs_DTM_lst <- glb_sprs_DTM_lst
# print(identical(sav_glb_corpus_lst, glb_corpus_lst))
# print(all.equal(length(sav_glb_corpus_lst), length(glb_corpus_lst)))
# print(all.equal(names(sav_glb_corpus_lst), names(glb_corpus_lst)))
# print(all.equal(sav_glb_corpus_lst[["Headline"]], glb_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
# print(identical(sav_sprs_DTM_lst, glb_sprs_DTM_lst))
rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df, terms_TfIdf_df)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_X_df <- as.data.frame(as.matrix(glb_full_DTM_lst[[txt_var]]))
terms_full_df <- get_DTM_terms(glb_full_DTM_lst[[txt_var]])
colnames(txt_full_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_full_X_df)), sep="")
rownames(txt_full_X_df) <- rownames(glb_allobs_df) # warning otherwise
if (glb_filter_txt_terms == "sparse") {
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
} else if (glb_filter_txt_terms == "top") {
txt_X_df <- txt_full_X_df[, terms_full_df$pos[1:glb_top_n[[txt_var]]], FALSE]
} else stop("glb_filter_txt_terms should be one of c('sparse', 'top') vs. '",
glb_filter_txt_terms, "'")
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.nwrds.log & .nwrds.unq.log
txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <-
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")]),
paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <- 0
# Create <txt_var>.nchrs.log
txt_X_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.nstopwrds.log & <txt_var>ratio.nstopwrds.nwrds
stop_words_rex_str <- paste0("\\b(", paste0(c(glb_append_stop_words[[txt_var]],
stopwords("english")), collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.nstopwrds.nwrds")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".nwrds", ".log")])
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create <txt_var>.P.mini & air
txt_X_df[, paste(txt_var_pfx, ".P.mini", sep="")] <-
as.integer(0 + mycount_pattern_occ("mini(?!m)", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.air", sep="")] <-
as.integer(0 + mycount_pattern_occ("(?<![fhp])air", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
# stop("Why is this happening ?")
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
rm(log_X_df, txt_X_df)
}## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: stringr
## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 20.109 21.344
## 3 extract.features_process.text 3 0 21.344 NA
## elapsed
## 2 1.235
## 3 NA
## [1] "Building glb_txt_lst..."
## [1] "running gsub for 10 (of 178): #\\bCentral African Republic\\b#..."
## [1] "running gsub for 20 (of 178): #\\bAlejandro G\\. Iñárritu#..."
## [1] "running gsub for 30 (of 178): #\\bC\\.A\\.A\\.#..."
## [1] "running gsub for 40 (of 178): #\\bCV\\.#..."
## [1] "running gsub for 50 (of 178): #\\bE\\.P\\.A\\.#..."
## [1] "running gsub for 60 (of 178): #\\bG\\.I\\. Joe#..."
## [1] "running gsub for 70 (of 178): #\\bISIS\\.#..."
## [1] "running gsub for 80 (of 178): #\\bJ\\.K\\. Simmons#..."
## [1] "running gsub for 90 (of 178): #\\bM\\. Henri Pol#..."
## [1] "running gsub for 100 (of 178): #\\bN\\.Y\\.S\\.E\\.#..."
## [1] "running gsub for 110 (of 178): #\\bR\\.B\\.S\\.#..."
## [1] "running gsub for 120 (of 178): #\\bSteven A\\. Cohen#..."
## [1] "running gsub for 130 (of 178): #\\bV\\.A\\.#..."
## [1] "running gsub for 140 (of 178): #\\bWall Street#..."
## [1] "running gsub for 150 (of 178): #\\bSaint( |-)((Laurent|Lucia)\\b)+#..."
## [1] "running gsub for 160 (of 178): #\\bSouth( |\\\\.)(America|American|Africa|African|Carolina|Dakota|Korea|Korean|Sudan)\\b#..."
## [1] "running gsub for 170 (of 178): #(\\w)-a-year#..."
## [1] "Remaining OK in descr.my:"## Loading required package: sqldf
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## pattern .n
## 1 OK 6
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN DEVICE: Problem with Apple ID"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN SCREEN"
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] "Remaining Acronyms in descr.my:"
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## pattern .n
## 1 CONDITION. 8
## 2 ONLY. 6
## 3 GB. 4
## 4 BOX. 2
## 5 CORNER. 2
## 6 ESN. 2
## 7 GOOD. 2
## 8 ICLOUD. 2
## 9 IPADS. 2
## 10 LOCKED. 2
## 11 LOCKS. 2
## 12 ONLY. 2
## 13 SCRATCHES. 2
## 14 TEARS. 2
## 15 USE. 2
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in descr.my: "
## pattern .n
## 2 New Open 3
## 4 New Condition 2
## 7 New Digitizer 1
## 8 New Opened 1
## 9 New Scratch 1
## 10 New Screen 1
## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in descr.my: "
## pattern .n
## 1 C Stock 3
## 2 W blue 1
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in descr.my: "
## label step_major
## 3 extract.features_process.text 3
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed
## 3 0 21.344 23.182 1.838
## 4 1 23.182 NA NA
## [1] "Remaining compound terms in descr.my: "
## label step_major
## 4 extract.features_process.text_reporting_compound_terms 3
## 5 extract.features_build.corpus 4
## step_minor bgn end elapsed
## 4 1 23.182 23.186 0.004
## 5 0 23.187 NA NA
## [1] "Building glb_corpus_lst..."
## [1] " Top_n stop TfIDf terms for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] "Rows: 729; Cols: 4"
## TfIdf term freq pos
## condition 208.1205 condition 498 149
## new 125.5866 new 156 433
## used 123.4473 used 240 695
## good 120.9670 good 197 289
## scratches 114.0567 scratches 254 570
## screen 106.6170 screen 210 572
## TfIdf term freq pos
## scratch 30.068378 scratch 25 568
## days 6.562908 days 4 184
## taken 5.878172 taken 6 649
## outer 5.557938 outer 5 458
## lot 3.602633 lot 2 386
## greeting 2.075117 greeting 2 294
## TfIdf term freq pos
## 975 1.1375583 975 1 16
## blemish 1.1375583 blemish 1 83
## cables 1.1375583 cables 1 106
## engravement 1.1375583 engravement 1 226
## handling 1.1375583 handling 1 304
## 79in 0.9479652 79in 1 15
## TfIdf term freq pos
## 975 1.1375583 975 1 16
## blemish 1.1375583 blemish 1 83
## cables 1.1375583 cables 1 106
## engravement 1.1375583 engravement 1 226
## handling 1.1375583 handling 1 304
## 79in 0.9479652 79in 1 15## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] " Top_n stem TfIDf terms for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] "Rows: 588; Cols: 4"
## TfIdf term freq pos
## condit 208.1066 condit 496 122
## use 146.5910 use 291 559
## scratch 128.3886 scratch 286 457
## new 125.5866 new 156 346
## good 121.0564 good 197 233
## ipad 107.4871 ipad 232 275
## TfIdf term freq pos
## set 17.367419 set 14 469
## purpos 3.372064 purpos 2 418
## first 2.939748 first 2 206
## spent 2.635069 spent 2 503
## oem 2.275117 oem 1 355
## refund 1.421948 refund 1 434
## TfIdf term freq pos
## remot 1.2639536 remot 1 437
## ringer 1.2639536 ringer 1 450
## septemb 1.2639536 septemb 1 468
## site 1.2639536 site 1 487
## 975 1.1375583 975 1 16
## 79in 0.9479652 79in 1 15
## TfIdf term freq pos
## remot 1.2639536 remot 1 437
## ringer 1.2639536 ringer 1 450
## septemb 1.2639536 septemb 1 468
## site 1.2639536 site 1 487
## 975 1.1375583 975 1 16
## 79in 0.9479652 79in 1 15## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [,1]
## terms.n.post.stop -0.08493199
## terms.n.post.stop.log -0.10435993
## TfIdf.sum.post.stop -0.12289243
## terms.n.post.stem -0.08417074
## terms.n.post.stem.log -0.10404513
## TfIdf.sum.post.stem -0.12021745
## terms.n.stem.stop.Ratio 0.04445385
## TfIdf.sum.stem.stop.Ratio 0.09957967
## label step_major step_minor bgn end
## 5 extract.features_build.corpus 4 0 23.187 34.391
## 6 extract.features_extract.DTM 5 0 34.392 NA
## elapsed
## 5 11.204
## 6 NA
## [1] "Extracting TfIDf terms for descr.my..."## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## label step_major step_minor bgn end elapsed
## 6 extract.features_extract.DTM 5 0 34.392 35.664 1.272
## 7 extract.features_report.DTM 6 0 35.665 NA NA## Loading required package: reshape2## [1] "Reporting TfIDf terms for descr.my..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 2657, terms: 588)>>
## Non-/sparse entries: 8269/1554047
## Sparsity : 99%
## Maximal term length: 16
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 2657, terms: 8)>>
## Non-/sparse entries: 2069/19187
## Sparsity : 90%
## Maximal term length: 7
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
## colorcol_name = "in.sprs"): converting in.sprs to class:factor
## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).
## Warning in rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df,
## terms_TfIdf_df): object 'full_TfIdf_mtrx' not found## label step_major step_minor bgn end elapsed
## 7 extract.features_report.DTM 6 0 35.665 37.797 2.132
## 8 extract.features_bind.DTM 7 0 37.797 NA NA
## [1] "Binding DTM for descr.my..."
## label step_major step_minor bgn end elapsed
## 8 extract.features_bind.DTM 7 0 37.797 38.222 0.425
## 9 extract.features_bind.DXM 8 0 38.222 NA NA
## [1] "Binding DXM for descr.my..."## Warning in rm(log_X_df, txt_X_df): object 'log_X_df' not found
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# dsp_obs(list(description.contains="mini(?!m)"), perl=TRUE, cols="D.P.mini", all=TRUE)
# dsp_obs(list(D.P.mini=1), cols="D.P.mini", all=TRUE)
# dsp_obs(list(D.P.mini=1, productline="Unknown"), cols="D.P.mini", all=TRUE)
# dsp_obs(list(description.contains="(?<![fhp])air"), perl=TRUE, all=TRUE)
# dsp_obs(list(description.contains="air"), perl=FALSE, cols="D.P.air", all=TRUE)
# dsp_obs(list(D.P.air=1, productline="Unknown"), cols="D.P.air", all=TRUE)
glb_allobs_df[(glb_allobs_df$D.P.mini == 1) & (glb_allobs_df$productline == "Unknown"), "prdline.my"] <- "iPad mini"
print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.mini", glb_rsp_var)))## prdline.my productline D.P.mini startprice .n
## 1 iPad 2 iPad 2 0 0.99 38
## 2 iPad mini iPad mini 0 0.99 30
## 3 Unknown Unknown 0 0.99 26
## 4 iPad 1 iPad 1 0 0.99 26
## 5 iPad 1 iPad 1 0 50.00 22
## 6 iPad mini iPad mini 0 150.00 20
## 7 iPad Air iPad Air 0 0.99 17
## 8 iPad 2 iPad 2 0 150.00 16
## 9 iPad 4 iPad 4 0 0.99 15
## 10 iPad mini iPad mini 0 100.00 14
## 11 iPad 2 iPad 2 0 100.00 13
## 12 iPad Air 2 iPad Air 2 0 0.99 13
## 13 iPad mini 2 iPad mini 2 0 0.99 13
## 14 iPad 1 iPad 1 0 80.00 12
## 15 iPad 3 iPad 3 0 0.99 12
## 16 iPad 3 iPad 3 0 200.00 12
## 17 iPad 1 iPad 1 0 90.00 11
## 18 iPad 2 iPad 2 0 175.00 11
## 19 Unknown Unknown 0 150.00 10
## 20 iPad 1 iPad 1 0 75.00 10
## 21 iPad 1 iPad 1 0 100.00 10
## 22 iPad 2 iPad 2 0 0.01 10
## 23 iPad 3 iPad 3 0 250.00 10
## 24 iPad mini iPad mini 0 50.00 10
## 25 iPad mini iPad mini 0 99.99 10
## 26 Unknown Unknown 0 100.00 9
## 27 iPad 2 iPad 2 0 99.99 9
## 28 iPad 2 iPad 2 0 149.99 9
## 29 iPad 2 iPad 2 0 199.99 9
## 30 iPad Air iPad Air 0 300.00 9
## 31 iPad mini iPad mini 0 199.99 9
## 32 Unknown Unknown 0 300.00 8
## 33 iPad 1 iPad 1 0 95.00 8
## 34 iPad 2 iPad 2 0 99.00 8
## 35 iPad 2 iPad 2 0 125.00 8
## 36 iPad 2 iPad 2 0 200.00 8
## 37 iPad 4 iPad 4 0 249.99 8
## 38 iPad Air 2 iPad Air 2 0 550.00 8
## 39 iPad mini iPad mini 0 200.00 8
## 40 iPad mini 2 iPad mini 2 0 350.00 8
## 41 Unknown Unknown 0 50.00 7
## 42 iPad 1 iPad 1 0 70.00 7
## 43 iPad 2 iPad 2 0 9.99 7
## 44 iPad 2 iPad 2 0 75.00 7
## 45 iPad 2 iPad 2 0 180.00 7
## 46 iPad 4 iPad 4 0 199.99 7
## 47 iPad mini iPad mini 0 99.00 7
## 48 iPad mini 3 iPad mini 3 0 0.99 7
## 49 iPad 1 iPad 1 0 1.00 6
## 50 iPad 2 iPad 2 0 50.00 6
## 51 iPad 2 iPad 2 0 160.00 6
## 52 iPad 4 iPad 4 0 100.00 6
## 53 iPad 4 iPad 4 0 150.00 6
## 54 iPad 4 iPad 4 0 279.99 6
## 55 iPad Air iPad Air 0 1.00 6
## 56 iPad Air iPad Air 0 200.00 6
## 57 iPad Air iPad Air 0 400.00 6
## 58 iPad Air 2 iPad Air 2 0 450.00 6
## 59 iPad mini iPad mini 0 75.00 6
## 60 iPad mini iPad mini 0 89.99 6
## 61 iPad mini iPad mini 0 159.99 6
## 62 iPad mini iPad mini 0 175.00 6
## 63 iPad 1 iPad 1 0 29.99 5
## 64 iPad 1 iPad 1 0 55.00 5
## 65 iPad 1 iPad 1 0 79.99 5
## 66 iPad 1 iPad 1 0 99.00 5
## 67 iPad 2 iPad 2 0 80.00 5
## 68 iPad 2 iPad 2 0 165.00 5
## 69 iPad 2 iPad 2 0 179.00 5
## 70 iPad 3 iPad 3 0 99.00 5
## 71 iPad 3 iPad 3 0 150.00 5
## 72 iPad 3 iPad 3 0 220.00 5
## 73 iPad 3 iPad 3 0 225.00 5
## 74 iPad 3 iPad 3 0 300.00 5
## 75 iPad 4 iPad 4 0 250.00 5
## 76 iPad 4 iPad 4 0 400.00 5
## 77 iPad Air iPad Air 0 100.00 5
## 78 iPad Air iPad Air 0 250.00 5
## 79 iPad Air iPad Air 0 350.00 5
## 80 iPad Air iPad Air 0 389.99 5
## 81 iPad Air 2 iPad Air 2 0 499.99 5
## 82 iPad mini iPad mini 0 1.00 5
## 83 iPad mini iPad mini 0 250.00 5
## 84 iPad mini iPad mini 0 350.00 5
## 85 iPad mini 2 iPad mini 2 0 200.00 5
## 86 iPad mini 2 iPad mini 2 0 225.00 5
## 87 Unknown Unknown 0 25.00 4
## 88 Unknown Unknown 0 149.99 4
## 89 Unknown Unknown 0 250.00 4
## 90 iPad 1 iPad 1 0 40.00 4
## 91 iPad 1 iPad 1 0 49.99 4
## 92 iPad 1 iPad 1 0 79.00 4
## 93 iPad 1 iPad 1 0 105.00 4
## 94 iPad 1 iPad 1 0 110.00 4
## 95 iPad 2 iPad 2 0 1.00 4
## 96 iPad 2 iPad 2 0 40.00 4
## 97 iPad 2 iPad 2 0 49.99 4
## 98 iPad 2 iPad 2 0 130.00 4
## 99 iPad 2 iPad 2 0 140.00 4
## 100 iPad 2 iPad 2 0 155.00 4
## 101 iPad 2 iPad 2 0 164.99 4
## 102 iPad 2 iPad 2 0 174.99 4
## 103 iPad 2 iPad 2 0 179.99 4
## 104 iPad 2 iPad 2 0 189.99 4
## 105 iPad 2 iPad 2 0 250.00 4
## 106 iPad 3 iPad 3 0 9.99 4
## 107 iPad 3 iPad 3 0 100.00 4
## 108 iPad 3 iPad 3 0 149.99 4
## 109 iPad 3 iPad 3 0 175.00 4
## 110 iPad 3 iPad 3 0 199.99 4
## 111 iPad 3 iPad 3 0 219.99 4
## 112 iPad 3 iPad 3 0 249.99 4
## 113 iPad 3 iPad 3 0 275.00 4
## 114 iPad 4 iPad 4 0 0.01 4
## 115 iPad 4 iPad 4 0 99.99 4
## 116 iPad 4 iPad 4 0 200.00 4
## 117 iPad 4 iPad 4 0 299.00 4
## 118 iPad Air iPad Air 0 199.99 4
## 119 iPad Air iPad Air 0 229.00 4
## 120 iPad Air iPad Air 0 279.99 4
## 121 iPad Air iPad Air 0 325.00 4
## 122 iPad Air iPad Air 0 329.99 4
## 123 iPad Air iPad Air 0 500.00 4
## 124 iPad Air 2 iPad Air 2 0 250.00 4
## 125 iPad Air 2 iPad Air 2 0 350.00 4
## 126 iPad Air 2 iPad Air 2 0 399.00 4
## 127 iPad Air 2 iPad Air 2 0 399.99 4
## 128 iPad Air 2 iPad Air 2 0 400.00 4
## 129 iPad Air 2 iPad Air 2 0 499.00 4
## 130 iPad Air 2 iPad Air 2 0 500.00 4
## 131 iPad Air 2 iPad Air 2 0 549.99 4
## 132 iPad mini iPad mini 0 119.99 4
## 133 iPad mini iPad mini 0 130.00 4
## 134 iPad mini iPad mini 0 199.00 4
## 135 iPad mini iPad mini 0 275.00 4
## 136 iPad mini iPad mini 0 300.00 4
## 137 iPad mini iPad mini 1 0.99 4
## 138 iPad mini 2 iPad mini 2 0 175.00 4
## 139 iPad mini 2 iPad mini 2 0 250.00 4
## 140 iPad mini 3 iPad mini 3 0 325.00 4
## 141 iPad mini 3 iPad mini 3 0 499.99 4
## 142 iPad mini 3 iPad mini 3 0 599.99 4
## 143 Unknown Unknown 0 15.00 3
## 144 Unknown Unknown 0 40.00 3
## 145 Unknown Unknown 0 75.00 3
## 146 Unknown Unknown 0 99.00 3
## 147 Unknown Unknown 0 120.00 3
## 148 Unknown Unknown 0 199.00 3
## 149 Unknown Unknown 0 199.99 3
## 150 Unknown Unknown 0 200.00 3
## 151 Unknown Unknown 0 249.00 3
## 152 Unknown Unknown 0 249.99 3
## 153 Unknown Unknown 0 299.99 3
## 154 Unknown Unknown 0 319.00 3
## 155 Unknown Unknown 0 350.00 3
## 156 iPad 1 iPad 1 0 0.01 3
## 157 iPad 1 iPad 1 0 19.99 3
## 158 iPad 1 iPad 1 0 20.00 3
## 159 iPad 1 iPad 1 0 25.00 3
## 160 iPad 1 iPad 1 0 30.00 3
## 161 iPad 1 iPad 1 0 36.95 3
## 162 iPad 1 iPad 1 0 65.00 3
## 163 iPad 1 iPad 1 0 84.99 3
## 164 iPad 1 iPad 1 0 85.00 3
## 165 iPad 1 iPad 1 0 89.00 3
## 166 iPad 1 iPad 1 0 99.99 3
## 167 iPad 1 iPad 1 0 119.99 3
## 168 iPad 1 iPad 1 0 150.00 3
## 169 iPad 1 iPad 1 0 180.00 3
## 170 iPad 2 iPad 2 0 30.00 3
## 171 iPad 2 iPad 2 0 70.00 3
## 172 iPad 2 iPad 2 0 85.00 3
## 173 iPad 2 iPad 2 0 89.99 3
## 174 iPad 2 iPad 2 0 90.00 3
## 175 iPad 2 iPad 2 0 120.00 3
## 176 iPad 2 iPad 2 0 129.95 3
## 177 iPad 2 iPad 2 0 129.99 3
## 178 iPad 2 iPad 2 0 139.00 3
## 179 iPad 2 iPad 2 0 149.00 3
## 180 iPad 2 iPad 2 0 149.95 3
## 181 iPad 2 iPad 2 0 154.00 3
## 182 iPad 2 iPad 2 0 159.99 3
## 183 iPad 2 iPad 2 0 169.00 3
## 184 iPad 2 iPad 2 0 249.97 3
## 185 iPad 2 iPad 2 0 275.00 3
## 186 iPad 2 iPad 2 0 300.00 3
## 187 iPad 3 iPad 3 0 1.00 3
## 188 iPad 3 iPad 3 0 10.00 3
## 189 iPad 3 iPad 3 0 99.99 3
## 190 iPad 3 iPad 3 0 128.00 3
## 191 iPad 3 iPad 3 0 185.00 3
## 192 iPad 3 iPad 3 0 187.50 3
## 193 iPad 3 iPad 3 0 199.00 3
## 194 iPad 4 iPad 4 0 50.00 3
## 195 iPad 4 iPad 4 0 225.00 3
## 196 iPad 4 iPad 4 0 259.99 3
## 197 iPad 4 iPad 4 0 275.00 3
## 198 iPad 4 iPad 4 0 280.00 3
## 199 iPad 4 iPad 4 0 300.00 3
## 200 iPad 4 iPad 4 0 320.00 3
## 201 iPad Air iPad Air 0 90.00 3
## 202 iPad Air iPad Air 0 290.00 3
## 203 iPad Air iPad Air 0 299.99 3
## 204 iPad Air iPad Air 0 320.00 3
## 205 iPad Air iPad Air 0 349.00 3
## 206 iPad Air iPad Air 0 379.00 3
## 207 iPad Air iPad Air 0 415.00 3
## 208 iPad Air iPad Air 0 449.99 3
## 209 iPad Air 2 iPad Air 2 0 1.00 3
## 210 iPad Air 2 iPad Air 2 0 50.00 3
## 211 iPad Air 2 iPad Air 2 0 199.99 3
## 212 iPad Air 2 iPad Air 2 0 425.00 3
## 213 iPad Air 2 iPad Air 2 0 439.99 3
## 214 iPad Air 2 iPad Air 2 0 480.00 3
## 215 iPad Air 2 iPad Air 2 0 525.00 3
## 216 iPad Air 2 iPad Air 2 0 560.00 3
## 217 iPad mini iPad mini 0 0.01 3
## 218 iPad mini iPad mini 0 20.00 3
## 219 iPad mini iPad mini 0 25.00 3
## 220 iPad mini iPad mini 0 45.00 3
## 221 iPad mini iPad mini 0 60.00 3
## 222 iPad mini iPad mini 0 125.00 3
## 223 iPad mini iPad mini 0 149.00 3
## 224 iPad mini iPad mini 0 160.00 3
## 225 iPad mini iPad mini 0 179.99 3
## 226 iPad mini iPad mini 0 189.99 3
## 227 iPad mini iPad mini 0 210.00 3
## 228 iPad mini iPad mini 0 249.99 3
## 229 iPad mini iPad mini 0 259.99 3
## 230 iPad mini iPad mini 0 290.00 3
## 231 iPad mini iPad mini 0 400.00 3
## 232 iPad mini 2 iPad mini 2 0 100.00 3
## 233 iPad mini 2 iPad mini 2 0 120.00 3
## 234 iPad mini 2 iPad mini 2 0 180.00 3
## 235 iPad mini 2 iPad mini 2 0 285.00 3
## 236 iPad mini 2 iPad mini 2 0 300.00 3
## 237 iPad mini 2 iPad mini 2 0 375.00 3
## 238 iPad mini 3 iPad mini 3 0 99.00 3
## 239 iPad mini 3 iPad mini 3 0 300.00 3
## 240 iPad mini 3 iPad mini 3 0 329.99 3
## 241 iPad mini 3 iPad mini 3 0 350.00 3
## 242 iPad mini 3 iPad mini 3 0 399.99 3
## 243 iPad mini 3 iPad mini 3 0 400.00 3
## 244 iPad mini 3 iPad mini 3 0 449.99 3
## 245 iPad mini 3 iPad mini 3 0 729.99 3
## 246 Unknown Unknown 0 5.00 2
## 247 Unknown Unknown 0 9.99 2
## 248 Unknown Unknown 0 19.99 2
## 249 Unknown Unknown 0 20.00 2
## 250 Unknown Unknown 0 39.99 2
## 251 Unknown Unknown 0 70.00 2
## 252 Unknown Unknown 0 79.95 2
## 253 Unknown Unknown 0 80.00 2
## 254 Unknown Unknown 0 99.99 2
## 255 Unknown Unknown 0 108.00 2
## 256 Unknown Unknown 0 159.99 2
## 257 Unknown Unknown 0 165.00 2
## 258 Unknown Unknown 0 169.99 2
## 259 Unknown Unknown 0 175.00 2
## 260 Unknown Unknown 0 185.00 2
## 261 Unknown Unknown 0 280.00 2
## 262 Unknown Unknown 0 319.99 2
## 263 Unknown Unknown 0 375.00 2
## 264 Unknown Unknown 0 399.00 2
## 265 Unknown Unknown 0 450.00 2
## 266 Unknown Unknown 0 500.00 2
## 267 Unknown Unknown 0 550.00 2
## 268 Unknown Unknown 0 599.99 2
## 269 Unknown Unknown 0 700.00 2
## 270 iPad 1 iPad 1 0 9.50 2
## 271 iPad 1 iPad 1 0 9.99 2
## 272 iPad 1 iPad 1 0 10.00 2
## 273 iPad 1 iPad 1 0 14.99 2
## 274 iPad 1 iPad 1 0 15.00 2
## 275 iPad 1 iPad 1 0 45.00 2
## 276 iPad 1 iPad 1 0 58.00 2
## 277 iPad 1 iPad 1 0 60.00 2
## 278 iPad 1 iPad 1 0 62.00 2
## 279 iPad 1 iPad 1 0 69.00 2
## 280 iPad 1 iPad 1 0 69.99 2
## 281 iPad 1 iPad 1 0 89.95 2
## 282 iPad 1 iPad 1 0 92.14 2
## 283 iPad 1 iPad 1 0 101.00 2
## 284 iPad 1 iPad 1 0 104.99 2
## 285 iPad 1 iPad 1 0 115.00 2
## 286 iPad 1 iPad 1 0 124.95 2
## 287 iPad 1 iPad 1 0 125.00 2
## 288 iPad 1 iPad 1 0 129.99 2
## 289 iPad 1 iPad 1 0 165.00 2
## 290 iPad 1 iPad 1 0 175.00 2
## 291 iPad 1 iPad 1 0 250.00 2
## 292 iPad 1 iPad 1 0 279.95 2
## 293 iPad 2 iPad 2 0 0.10 2
## 294 iPad 2 iPad 2 0 15.00 2
## 295 iPad 2 iPad 2 0 19.95 2
## 296 iPad 2 iPad 2 0 59.99 2
## 297 iPad 2 iPad 2 0 65.00 2
## 298 iPad 2 iPad 2 0 69.99 2
## 299 iPad 2 iPad 2 0 74.99 2
## 300 iPad 2 iPad 2 0 89.00 2
## 301 iPad 2 iPad 2 0 95.00 2
## 302 iPad 2 iPad 2 0 119.99 2
## 303 iPad 2 iPad 2 0 128.00 2
## 304 iPad 2 iPad 2 0 135.00 2
## 305 iPad 2 iPad 2 0 144.99 2
## 306 iPad 2 iPad 2 0 145.00 2
## 307 iPad 2 iPad 2 0 149.97 2
## 308 iPad 2 iPad 2 0 150.99 2
## 309 iPad 2 iPad 2 0 162.00 2
## 310 iPad 2 iPad 2 0 169.99 2
## 311 iPad 2 iPad 2 0 170.00 2
## 312 iPad 2 iPad 2 0 172.00 2
## 313 iPad 2 iPad 2 0 179.95 2
## 314 iPad 2 iPad 2 0 204.00 2
## 315 iPad 2 iPad 2 0 220.00 2
## 316 iPad 2 iPad 2 0 350.00 2
## 317 iPad 3 iPad 3 0 0.01 2
## 318 iPad 3 iPad 3 0 25.00 2
## 319 iPad 3 iPad 3 0 49.99 2
## 320 iPad 3 iPad 3 0 89.99 2
## 321 iPad 3 iPad 3 0 99.95 2
## 322 iPad 3 iPad 3 0 125.00 2
## 323 iPad 3 iPad 3 0 140.00 2
## 324 iPad 3 iPad 3 0 179.99 2
## 325 iPad 3 iPad 3 0 180.00 2
## 326 iPad 3 iPad 3 0 209.99 2
## 327 iPad 3 iPad 3 0 215.00 2
## 328 iPad 3 iPad 3 0 229.99 2
## 329 iPad 3 iPad 3 0 239.88 2
## 330 iPad 3 iPad 3 0 239.99 2
## 331 iPad 3 iPad 3 0 299.00 2
## 332 iPad 3 iPad 3 0 314.99 2
## 333 iPad 3 iPad 3 0 450.00 2
## 334 iPad 4 iPad 4 0 80.00 2
## 335 iPad 4 iPad 4 0 99.98 2
## 336 iPad 4 iPad 4 0 107.00 2
## 337 iPad 4 iPad 4 0 125.00 2
## 338 iPad 4 iPad 4 0 195.00 2
## 339 iPad 4 iPad 4 0 199.00 2
## 340 iPad 4 iPad 4 0 209.00 2
## 341 iPad 4 iPad 4 0 240.00 2
## 342 iPad 4 iPad 4 0 255.00 2
## 343 iPad 4 iPad 4 0 265.00 2
## 344 iPad 4 iPad 4 0 269.99 2
## 345 iPad 4 iPad 4 0 285.00 2
## 346 iPad 4 iPad 4 0 295.00 2
## 347 iPad 4 iPad 4 0 299.99 2
## 348 iPad 4 iPad 4 0 305.00 2
## 349 iPad 4 iPad 4 0 309.99 2
## 350 iPad 4 iPad 4 0 310.00 2
## 351 iPad 4 iPad 4 0 315.00 2
## 352 iPad 4 iPad 4 0 324.99 2
## 353 iPad 4 iPad 4 0 325.00 2
## 354 iPad 4 iPad 4 0 344.00 2
## 355 iPad 4 iPad 4 0 350.00 2
## 356 iPad 4 iPad 4 0 367.97 2
## 357 iPad 4 iPad 4 0 375.00 2
## 358 iPad 4 iPad 4 0 500.00 2
## 359 iPad 4 iPad 4 0 588.18 2
## 360 iPad Air iPad Air 0 49.99 2
## 361 iPad Air iPad Air 0 75.00 2
## 362 iPad Air iPad Air 0 89.99 2
## 363 iPad Air iPad Air 0 99.99 2
## 364 iPad Air iPad Air 0 149.99 2
## 365 iPad Air iPad Air 0 199.00 2
## 366 iPad Air iPad Air 0 209.00 2
## 367 iPad Air iPad Air 0 245.00 2
## 368 iPad Air iPad Air 0 249.98 2
## 369 iPad Air iPad Air 0 265.00 2
## 370 iPad Air iPad Air 0 279.00 2
## 371 iPad Air iPad Air 0 280.00 2
## 372 iPad Air iPad Air 0 299.00 2
## 373 iPad Air iPad Air 0 319.00 2
## 374 iPad Air iPad Air 0 319.95 2
## 375 iPad Air iPad Air 0 319.99 2
## 376 iPad Air iPad Air 0 320.99 2
## 377 iPad Air iPad Air 0 339.00 2
## 378 iPad Air iPad Air 0 349.99 2
## 379 iPad Air iPad Air 0 369.99 2
## 380 iPad Air iPad Air 0 375.00 2
## 381 iPad Air iPad Air 0 379.99 2
## 382 iPad Air iPad Air 0 398.99 2
## 383 iPad Air iPad Air 0 399.99 2
## 384 iPad Air iPad Air 0 450.00 2
## 385 iPad Air iPad Air 0 579.99 2
## 386 iPad Air iPad Air 0 648.00 2
## 387 iPad Air iPad Air 0 750.00 2
## 388 iPad Air 2 iPad Air 2 0 99.99 2
## 389 iPad Air 2 iPad Air 2 0 200.00 2
## 390 iPad Air 2 iPad Air 2 0 260.00 2
## 391 iPad Air 2 iPad Air 2 0 300.00 2
## 392 iPad Air 2 iPad Air 2 0 349.99 2
## 393 iPad Air 2 iPad Air 2 0 379.99 2
## 394 iPad Air 2 iPad Air 2 0 419.00 2
## 395 iPad Air 2 iPad Air 2 0 449.00 2
## 396 iPad Air 2 iPad Air 2 0 465.99 2
## 397 iPad Air 2 iPad Air 2 0 475.00 2
## 398 iPad Air 2 iPad Air 2 0 549.00 2
## 399 iPad Air 2 iPad Air 2 0 559.99 2
## 400 iPad Air 2 iPad Air 2 0 575.00 2
## 401 iPad Air 2 iPad Air 2 0 639.00 2
## 402 iPad Air 2 iPad Air 2 0 639.99 2
## 403 iPad Air 2 iPad Air 2 0 650.00 2
## 404 iPad Air 2 iPad Air 2 0 729.99 2
## 405 iPad Air 2 iPad Air 2 0 749.00 2
## 406 iPad Air 2 iPad Air 2 0 749.95 2
## 407 iPad Air 2 iPad Air 2 0 800.00 2
## 408 iPad mini Unknown 1 149.99 2
## 409 iPad mini iPad mini 0 5.00 2
## 410 iPad mini iPad mini 0 10.00 2
## 411 iPad mini iPad mini 0 30.00 2
## 412 iPad mini iPad mini 0 70.00 2
## 413 iPad mini iPad mini 0 85.00 2
## 414 iPad mini iPad mini 0 90.00 2
## 415 iPad mini iPad mini 0 99.95 2
## 416 iPad mini iPad mini 0 114.99 2
## 417 iPad mini iPad mini 0 115.00 2
## 418 iPad mini iPad mini 0 139.99 2
## 419 iPad mini iPad mini 0 155.00 2
## 420 iPad mini iPad mini 0 165.00 2
## 421 iPad mini iPad mini 0 174.99 2
## 422 iPad mini iPad mini 0 215.00 2
## 423 iPad mini iPad mini 0 219.99 2
## 424 iPad mini iPad mini 0 225.00 2
## 425 iPad mini iPad mini 0 230.00 2
## 426 iPad mini iPad mini 0 239.99 2
## 427 iPad mini iPad mini 0 249.00 2
## 428 iPad mini iPad mini 0 258.98 2
## 429 iPad mini iPad mini 0 280.00 2
## 430 iPad mini iPad mini 0 285.00 2
## 431 iPad mini iPad mini 0 299.99 2
## 432 iPad mini iPad mini 1 179.00 2
## 433 iPad mini iPad mini 1 199.00 2
## 434 iPad mini 2 iPad mini 2 0 1.00 2
## 435 iPad mini 2 iPad mini 2 0 99.00 2
## 436 iPad mini 2 iPad mini 2 0 99.99 2
## 437 iPad mini 2 iPad mini 2 0 187.99 2
## 438 iPad mini 2 iPad mini 2 0 230.00 2
## 439 iPad mini 2 iPad mini 2 0 235.00 2
## 440 iPad mini 2 iPad mini 2 0 269.00 2
## 441 iPad mini 2 iPad mini 2 0 275.00 2
## 442 iPad mini 2 iPad mini 2 0 280.00 2
## 443 iPad mini 2 iPad mini 2 0 289.00 2
## 444 iPad mini 2 iPad mini 2 0 299.00 2
## 445 iPad mini 2 iPad mini 2 0 315.00 2
## 446 iPad mini 2 iPad mini 2 0 325.00 2
## 447 iPad mini 2 iPad mini 2 0 329.00 2
## 448 iPad mini 2 iPad mini 2 0 329.99 2
## 449 iPad mini 2 iPad mini 2 0 349.99 2
## 450 iPad mini 2 iPad mini 2 0 399.99 2
## 451 iPad mini 2 iPad mini 2 0 499.00 2
## 452 iPad mini 3 iPad mini 3 0 0.01 2
## 453 iPad mini 3 iPad mini 3 0 199.00 2
## 454 iPad mini 3 iPad mini 3 0 284.99 2
## 455 iPad mini 3 iPad mini 3 0 299.99 2
## 456 iPad mini 3 iPad mini 3 0 345.00 2
## 457 iPad mini 3 iPad mini 3 0 349.00 2
## 458 iPad mini 3 iPad mini 3 0 375.00 2
## 459 iPad mini 3 iPad mini 3 0 389.99 2
## 460 iPad mini 3 iPad mini 3 0 498.88 2
## 461 iPad mini 3 iPad mini 3 0 500.00 2
## 462 Unknown Unknown 0 0.01 1
## 463 Unknown Unknown 0 1.00 1
## 464 Unknown Unknown 0 2.99 1
## 465 Unknown Unknown 0 3.99 1
## 466 Unknown Unknown 0 4.69 1
## 467 Unknown Unknown 0 4.99 1
## 468 Unknown Unknown 0 5.65 1
## 469 Unknown Unknown 0 7.99 1
## 470 Unknown Unknown 0 8.99 1
## 471 Unknown Unknown 0 9.95 1
## 472 Unknown Unknown 0 10.00 1
## 473 Unknown Unknown 0 14.00 1
## 474 Unknown Unknown 0 14.49 1
## 475 Unknown Unknown 0 14.99 1
## 476 Unknown Unknown 0 17.75 1
## 477 Unknown Unknown 0 24.99 1
## 478 Unknown Unknown 0 28.00 1
## 479 Unknown Unknown 0 28.75 1
## 480 Unknown Unknown 0 30.00 1
## 481 Unknown Unknown 0 30.99 1
## 482 Unknown Unknown 0 32.95 1
## 483 Unknown Unknown 0 35.00 1
## 484 Unknown Unknown 0 37.98 1
## 485 Unknown Unknown 0 41.00 1
## 486 Unknown Unknown 0 43.20 1
## 487 Unknown Unknown 0 44.99 1
## 488 Unknown Unknown 0 45.00 1
## 489 Unknown Unknown 0 49.95 1
## 490 Unknown Unknown 0 49.99 1
## 491 Unknown Unknown 0 51.99 1
## 492 Unknown Unknown 0 52.25 1
## 493 Unknown Unknown 0 57.50 1
## 494 Unknown Unknown 0 58.00 1
## 495 Unknown Unknown 0 59.95 1
## 496 Unknown Unknown 0 59.99 1
## 497 Unknown Unknown 0 63.00 1
## 498 Unknown Unknown 0 65.00 1
## 499 Unknown Unknown 0 69.00 1
## 500 Unknown Unknown 0 70.99 1
## 501 Unknown Unknown 0 76.00 1
## 502 Unknown Unknown 0 87.00 1
## 503 Unknown Unknown 0 89.00 1
## 504 Unknown Unknown 0 90.00 1
## 505 Unknown Unknown 0 91.00 1
## 506 Unknown Unknown 0 99.46 1
## 507 Unknown Unknown 0 109.99 1
## 508 Unknown Unknown 0 110.00 1
## 509 Unknown Unknown 0 114.48 1
## 510 Unknown Unknown 0 116.33 1
## 511 Unknown Unknown 0 118.84 1
## 512 Unknown Unknown 0 118.95 1
## 513 Unknown Unknown 0 119.99 1
## 514 Unknown Unknown 0 124.99 1
## 515 Unknown Unknown 0 125.00 1
## 516 Unknown Unknown 0 127.95 1
## 517 Unknown Unknown 0 137.95 1
## 518 Unknown Unknown 0 139.00 1
## 519 Unknown Unknown 0 139.99 1
## 520 Unknown Unknown 0 145.00 1
## 521 Unknown Unknown 0 149.98 1
## 522 Unknown Unknown 0 150.87 1
## 523 Unknown Unknown 0 154.99 1
## 524 Unknown Unknown 0 155.00 1
## 525 Unknown Unknown 0 167.38 1
## 526 Unknown Unknown 0 168.00 1
## 527 Unknown Unknown 0 170.00 1
## 528 Unknown Unknown 0 174.00 1
## 529 Unknown Unknown 0 177.99 1
## 530 Unknown Unknown 0 179.99 1
## 531 Unknown Unknown 0 180.00 1
## 532 Unknown Unknown 0 182.77 1
## 533 Unknown Unknown 0 186.00 1
## 534 Unknown Unknown 0 188.00 1
## 535 Unknown Unknown 0 190.99 1
## 536 Unknown Unknown 0 193.15 1
## 537 Unknown Unknown 0 195.00 1
## 538 Unknown Unknown 0 196.79 1
## 539 Unknown Unknown 0 199.97 1
## 540 Unknown Unknown 0 210.00 1
## 541 Unknown Unknown 0 214.95 1
## 542 Unknown Unknown 0 215.00 1
## 543 Unknown Unknown 0 220.00 1
## 544 Unknown Unknown 0 224.00 1
## 545 Unknown Unknown 0 225.00 1
## 546 Unknown Unknown 0 229.00 1
## 547 Unknown Unknown 0 229.95 1
## 548 Unknown Unknown 0 229.99 1
## 549 Unknown Unknown 0 230.00 1
## 550 Unknown Unknown 0 232.99 1
## 551 Unknown Unknown 0 239.99 1
## 552 Unknown Unknown 0 255.00 1
## 553 Unknown Unknown 0 259.00 1
## 554 Unknown Unknown 0 259.99 1
## 555 Unknown Unknown 0 260.00 1
## 556 Unknown Unknown 0 265.00 1
## 557 Unknown Unknown 0 275.00 1
## 558 Unknown Unknown 0 276.99 1
## 559 Unknown Unknown 0 279.00 1
## 560 Unknown Unknown 0 285.00 1
## 561 Unknown Unknown 0 289.00 1
## 562 Unknown Unknown 0 295.00 1
## 563 Unknown Unknown 0 309.98 1
## 564 Unknown Unknown 0 318.00 1
## 565 Unknown Unknown 0 325.00 1
## 566 Unknown Unknown 0 329.99 1
## 567 Unknown Unknown 0 334.99 1
## 568 Unknown Unknown 0 339.00 1
## 569 Unknown Unknown 0 340.00 1
## 570 Unknown Unknown 0 347.00 1
## 571 Unknown Unknown 0 349.99 1
## 572 Unknown Unknown 0 380.00 1
## 573 Unknown Unknown 0 389.00 1
## 574 Unknown Unknown 0 395.00 1
## 575 Unknown Unknown 0 408.60 1
## 576 Unknown Unknown 0 415.00 1
## 577 Unknown Unknown 0 419.99 1
## 578 Unknown Unknown 0 425.99 1
## 579 Unknown Unknown 0 426.30 1
## 580 Unknown Unknown 0 439.98 1
## 581 Unknown Unknown 0 470.00 1
## 582 Unknown Unknown 0 473.60 1
## 583 Unknown Unknown 0 480.00 1
## 584 Unknown Unknown 0 520.90 1
## 585 Unknown Unknown 0 535.00 1
## 586 Unknown Unknown 0 554.77 1
## 587 Unknown Unknown 0 561.53 1
## 588 Unknown Unknown 0 590.00 1
## 589 Unknown Unknown 0 595.00 1
## 590 Unknown Unknown 0 599.00 1
## 591 Unknown Unknown 0 639.99 1
## 592 Unknown Unknown 0 640.00 1
## 593 Unknown Unknown 0 679.99 1
## 594 iPad 1 iPad 1 0 9.95 1
## 595 iPad 1 iPad 1 0 19.95 1
## 596 iPad 1 iPad 1 0 29.95 1
## 597 iPad 1 iPad 1 0 33.00 1
## 598 iPad 1 iPad 1 0 35.00 1
## 599 iPad 1 iPad 1 0 39.99 1
## 600 iPad 1 iPad 1 0 42.00 1
## 601 iPad 1 iPad 1 0 48.00 1
## 602 iPad 1 iPad 1 0 48.99 1
## 603 iPad 1 iPad 1 0 49.49 1
## 604 iPad 1 iPad 1 0 52.99 1
## 605 iPad 1 iPad 1 0 54.99 1
## 606 iPad 1 iPad 1 0 55.66 1
## 607 iPad 1 iPad 1 0 56.00 1
## 608 iPad 1 iPad 1 0 59.00 1
## 609 iPad 1 iPad 1 0 59.99 1
## 610 iPad 1 iPad 1 0 64.99 1
## 611 iPad 1 iPad 1 0 72.00 1
## 612 iPad 1 iPad 1 0 74.00 1
## 613 iPad 1 iPad 1 0 74.50 1
## 614 iPad 1 iPad 1 0 74.99 1
## 615 iPad 1 iPad 1 0 78.00 1
## 616 iPad 1 iPad 1 0 79.94 1
## 617 iPad 1 iPad 1 0 82.95 1
## 618 iPad 1 iPad 1 0 82.98 1
## 619 iPad 1 iPad 1 0 85.95 1
## 620 iPad 1 iPad 1 0 89.50 1
## 621 iPad 1 iPad 1 0 91.00 1
## 622 iPad 1 iPad 1 0 92.00 1
## 623 iPad 1 iPad 1 0 93.00 1
## 624 iPad 1 iPad 1 0 94.99 1
## 625 iPad 1 iPad 1 0 96.00 1
## 626 iPad 1 iPad 1 0 98.00 1
## 627 iPad 1 iPad 1 0 99.94 1
## 628 iPad 1 iPad 1 0 102.00 1
## 629 iPad 1 iPad 1 0 104.70 1
## 630 iPad 1 iPad 1 0 109.00 1
## 631 iPad 1 iPad 1 0 109.98 1
## 632 iPad 1 iPad 1 0 112.99 1
## 633 iPad 1 iPad 1 0 114.94 1
## 634 iPad 1 iPad 1 0 119.00 1
## 635 iPad 1 iPad 1 0 120.00 1
## 636 iPad 1 iPad 1 0 120.02 1
## 637 iPad 1 iPad 1 0 124.99 1
## 638 iPad 1 iPad 1 0 129.95 1
## 639 iPad 1 iPad 1 0 130.00 1
## 640 iPad 1 iPad 1 0 145.00 1
## 641 iPad 1 iPad 1 0 149.00 1
## 642 iPad 1 iPad 1 0 149.95 1
## 643 iPad 1 iPad 1 0 149.98 1
## 644 iPad 1 iPad 1 0 149.99 1
## 645 iPad 1 iPad 1 0 155.00 1
## 646 iPad 1 iPad 1 0 159.95 1
## 647 iPad 1 iPad 1 0 169.95 1
## 648 iPad 1 iPad 1 0 170.00 1
## 649 iPad 1 iPad 1 0 174.99 1
## 650 iPad 1 iPad 1 0 190.45 1
## 651 iPad 1 iPad 1 0 198.00 1
## 652 iPad 1 iPad 1 0 199.99 1
## 653 iPad 1 iPad 1 0 200.00 1
## 654 iPad 1 iPad 1 0 209.90 1
## 655 iPad 1 iPad 1 0 220.00 1
## 656 iPad 1 iPad 1 0 225.00 1
## 657 iPad 1 iPad 1 0 227.00 1
## 658 iPad 1 iPad 1 0 229.00 1
## 659 iPad 1 iPad 1 0 229.97 1
## 660 iPad 1 iPad 1 0 229.99 1
## 661 iPad 1 iPad 1 0 235.00 1
## 662 iPad 1 iPad 1 0 245.00 1
## 663 iPad 1 iPad 1 0 269.99 1
## 664 iPad 1 iPad 1 0 275.00 1
## 665 iPad 1 iPad 1 0 289.95 1
## 666 iPad 1 iPad 1 0 499.00 1
## 667 iPad 2 iPad 2 0 5.00 1
## 668 iPad 2 iPad 2 0 20.00 1
## 669 iPad 2 iPad 2 0 25.00 1
## 670 iPad 2 iPad 2 0 29.95 1
## 671 iPad 2 iPad 2 0 29.99 1
## 672 iPad 2 iPad 2 0 39.99 1
## 673 iPad 2 iPad 2 0 45.00 1
## 674 iPad 2 iPad 2 0 49.00 1
## 675 iPad 2 iPad 2 0 60.00 1
## 676 iPad 2 iPad 2 0 66.99 1
## 677 iPad 2 iPad 2 0 69.50 1
## 678 iPad 2 iPad 2 0 69.95 1
## 679 iPad 2 iPad 2 0 71.99 1
## 680 iPad 2 iPad 2 0 72.00 1
## 681 iPad 2 iPad 2 0 74.00 1
## 682 iPad 2 iPad 2 0 79.00 1
## 683 iPad 2 iPad 2 0 79.97 1
## 684 iPad 2 iPad 2 0 89.95 1
## 685 iPad 2 iPad 2 0 92.00 1
## 686 iPad 2 iPad 2 0 92.49 1
## 687 iPad 2 iPad 2 0 96.00 1
## 688 iPad 2 iPad 2 0 97.50 1
## 689 iPad 2 iPad 2 0 104.00 1
## 690 iPad 2 iPad 2 0 106.95 1
## 691 iPad 2 iPad 2 0 109.99 1
## 692 iPad 2 iPad 2 0 110.00 1
## 693 iPad 2 iPad 2 0 111.00 1
## 694 iPad 2 iPad 2 0 111.50 1
## 695 iPad 2 iPad 2 0 112.00 1
## 696 iPad 2 iPad 2 0 115.00 1
## 697 iPad 2 iPad 2 0 119.95 1
## 698 iPad 2 iPad 2 0 121.00 1
## 699 iPad 2 iPad 2 0 124.00 1
## 700 iPad 2 iPad 2 0 127.99 1
## 701 iPad 2 iPad 2 0 134.34 1
## 702 iPad 2 iPad 2 0 134.95 1
## 703 iPad 2 iPad 2 0 139.50 1
## 704 iPad 2 iPad 2 0 139.98 1
## 705 iPad 2 iPad 2 0 141.09 1
## 706 iPad 2 iPad 2 0 142.25 1
## 707 iPad 2 iPad 2 0 142.49 1
## 708 iPad 2 iPad 2 0 145.49 1
## 709 iPad 2 iPad 2 0 146.99 1
## 710 iPad 2 iPad 2 0 147.59 1
## 711 iPad 2 iPad 2 0 147.72 1
## 712 iPad 2 iPad 2 0 152.00 1
## 713 iPad 2 iPad 2 0 153.95 1
## 714 iPad 2 iPad 2 0 153.99 1
## 715 iPad 2 iPad 2 0 157.00 1
## 716 iPad 2 iPad 2 0 158.99 1
## 717 iPad 2 iPad 2 0 159.93 1
## 718 iPad 2 iPad 2 0 159.94 1
## 719 iPad 2 iPad 2 0 159.95 1
## 720 iPad 2 iPad 2 0 164.00 1
## 721 iPad 2 iPad 2 0 169.98 1
## 722 iPad 2 iPad 2 0 171.00 1
## 723 iPad 2 iPad 2 0 173.00 1
## 724 iPad 2 iPad 2 0 182.00 1
## 725 iPad 2 iPad 2 0 184.99 1
## 726 iPad 2 iPad 2 0 189.00 1
## 727 iPad 2 iPad 2 0 189.85 1
## 728 iPad 2 iPad 2 0 189.95 1
## 729 iPad 2 iPad 2 0 190.00 1
## 730 iPad 2 iPad 2 0 190.45 1
## 731 iPad 2 iPad 2 0 193.00 1
## 732 iPad 2 iPad 2 0 194.00 1
## 733 iPad 2 iPad 2 0 194.85 1
## 734 iPad 2 iPad 2 0 194.95 1
## 735 iPad 2 iPad 2 0 195.00 1
## 736 iPad 2 iPad 2 0 198.98 1
## 737 iPad 2 iPad 2 0 199.00 1
## 738 iPad 2 iPad 2 0 204.95 1
## 739 iPad 2 iPad 2 0 209.00 1
## 740 iPad 2 iPad 2 0 210.00 1
## 741 iPad 2 iPad 2 0 211.95 1
## 742 iPad 2 iPad 2 0 212.99 1
## 743 iPad 2 iPad 2 0 215.00 1
## 744 iPad 2 iPad 2 0 215.99 1
## 745 iPad 2 iPad 2 0 219.85 1
## 746 iPad 2 iPad 2 0 225.00 1
## 747 iPad 2 iPad 2 0 227.95 1
## 748 iPad 2 iPad 2 0 228.59 1
## 749 iPad 2 iPad 2 0 229.99 1
## 750 iPad 2 iPad 2 0 234.99 1
## 751 iPad 2 iPad 2 0 235.00 1
## 752 iPad 2 iPad 2 0 239.00 1
## 753 iPad 2 iPad 2 0 239.99 1
## 754 iPad 2 iPad 2 0 249.95 1
## 755 iPad 2 iPad 2 0 255.00 1
## 756 iPad 2 iPad 2 0 269.94 1
## 757 iPad 2 iPad 2 0 279.00 1
## 758 iPad 2 iPad 2 0 279.99 1
## 759 iPad 2 iPad 2 0 280.00 1
## 760 iPad 2 iPad 2 0 288.00 1
## 761 iPad 2 iPad 2 0 289.99 1
## 762 iPad 2 iPad 2 0 294.99 1
## 763 iPad 2 iPad 2 0 298.97 1
## 764 iPad 2 iPad 2 0 310.00 1
## 765 iPad 2 iPad 2 0 315.00 1
## 766 iPad 2 iPad 2 0 330.00 1
## 767 iPad 2 iPad 2 0 340.00 1
## 768 iPad 2 iPad 2 0 349.99 1
## 769 iPad 2 iPad 2 0 393.00 1
## 770 iPad 2 iPad 2 0 395.00 1
## 771 iPad 2 iPad 2 0 396.00 1
## 772 iPad 2 iPad 2 0 406.00 1
## 773 iPad 2 iPad 2 0 417.00 1
## 774 iPad 2 iPad 2 0 425.00 1
## 775 iPad 2 iPad 2 0 525.00 1
## 776 iPad 2 iPad 2 0 700.00 1
## 777 iPad 3 iPad 3 0 0.50 1
## 778 iPad 3 iPad 3 0 5.00 1
## 779 iPad 3 iPad 3 0 7.99 1
## 780 iPad 3 iPad 3 0 45.00 1
## 781 iPad 3 iPad 3 0 50.00 1
## 782 iPad 3 iPad 3 0 69.69 1
## 783 iPad 3 iPad 3 0 71.00 1
## 784 iPad 3 iPad 3 0 75.00 1
## 785 iPad 3 iPad 3 0 80.00 1
## 786 iPad 3 iPad 3 0 85.00 1
## 787 iPad 3 iPad 3 0 95.00 1
## 788 iPad 3 iPad 3 0 105.00 1
## 789 iPad 3 iPad 3 0 115.00 1
## 790 iPad 3 iPad 3 0 120.00 1
## 791 iPad 3 iPad 3 0 129.00 1
## 792 iPad 3 iPad 3 0 134.61 1
## 793 iPad 3 iPad 3 0 159.00 1
## 794 iPad 3 iPad 3 0 159.99 1
## 795 iPad 3 iPad 3 0 165.00 1
## 796 iPad 3 iPad 3 0 179.00 1
## 797 iPad 3 iPad 3 0 179.95 1
## 798 iPad 3 iPad 3 0 184.50 1
## 799 iPad 3 iPad 3 0 184.95 1
## 800 iPad 3 iPad 3 0 188.99 1
## 801 iPad 3 iPad 3 0 189.99 1
## 802 iPad 3 iPad 3 0 190.00 1
## 803 iPad 3 iPad 3 0 196.00 1
## 804 iPad 3 iPad 3 0 199.69 1
## 805 iPad 3 iPad 3 0 200.29 1
## 806 iPad 3 iPad 3 0 204.00 1
## 807 iPad 3 iPad 3 0 210.99 1
## 808 iPad 3 iPad 3 0 214.99 1
## 809 iPad 3 iPad 3 0 219.95 1
## 810 iPad 3 iPad 3 0 228.88 1
## 811 iPad 3 iPad 3 0 229.00 1
## 812 iPad 3 iPad 3 0 229.98 1
## 813 iPad 3 iPad 3 0 234.00 1
## 814 iPad 3 iPad 3 0 235.00 1
## 815 iPad 3 iPad 3 0 235.99 1
## 816 iPad 3 iPad 3 0 246.00 1
## 817 iPad 3 iPad 3 0 248.00 1
## 818 iPad 3 iPad 3 0 249.00 1
## 819 iPad 3 iPad 3 0 249.97 1
## 820 iPad 3 iPad 3 0 252.99 1
## 821 iPad 3 iPad 3 0 259.00 1
## 822 iPad 3 iPad 3 0 269.95 1
## 823 iPad 3 iPad 3 0 289.00 1
## 824 iPad 3 iPad 3 0 289.98 1
## 825 iPad 3 iPad 3 0 290.00 1
## 826 iPad 3 iPad 3 0 295.00 1
## 827 iPad 3 iPad 3 0 298.00 1
## 828 iPad 3 iPad 3 0 299.99 1
## 829 iPad 3 iPad 3 0 319.95 1
## 830 iPad 3 iPad 3 0 339.00 1
## 831 iPad 3 iPad 3 0 350.00 1
## 832 iPad 3 iPad 3 0 380.00 1
## 833 iPad 3 iPad 3 0 395.00 1
## 834 iPad 3 iPad 3 0 463.26 1
## 835 iPad 3 iPad 3 0 494.50 1
## 836 iPad 3 iPad 3 0 600.00 1
## 837 iPad 3 iPad 3 0 649.99 1
## 838 iPad 3 iPad 3 0 699.95 1
## 839 iPad 4 iPad 4 0 1.00 1
## 840 iPad 4 iPad 4 0 7.99 1
## 841 iPad 4 iPad 4 0 8.00 1
## 842 iPad 4 iPad 4 0 10.00 1
## 843 iPad 4 iPad 4 0 20.00 1
## 844 iPad 4 iPad 4 0 35.00 1
## 845 iPad 4 iPad 4 0 38.99 1
## 846 iPad 4 iPad 4 0 39.00 1
## 847 iPad 4 iPad 4 0 65.00 1
## 848 iPad 4 iPad 4 0 79.99 1
## 849 iPad 4 iPad 4 0 99.75 1
## 850 iPad 4 iPad 4 0 99.95 1
## 851 iPad 4 iPad 4 0 115.00 1
## 852 iPad 4 iPad 4 0 119.88 1
## 853 iPad 4 iPad 4 0 119.99 1
## 854 iPad 4 iPad 4 0 139.99 1
## 855 iPad 4 iPad 4 0 144.50 1
## 856 iPad 4 iPad 4 0 149.98 1
## 857 iPad 4 iPad 4 0 155.99 1
## 858 iPad 4 iPad 4 0 160.00 1
## 859 iPad 4 iPad 4 0 174.95 1
## 860 iPad 4 iPad 4 0 185.00 1
## 861 iPad 4 iPad 4 0 189.00 1
## 862 iPad 4 iPad 4 0 215.00 1
## 863 iPad 4 iPad 4 0 218.00 1
## 864 iPad 4 iPad 4 0 219.99 1
## 865 iPad 4 iPad 4 0 220.00 1
## 866 iPad 4 iPad 4 0 224.98 1
## 867 iPad 4 iPad 4 0 224.99 1
## 868 iPad 4 iPad 4 0 229.00 1
## 869 iPad 4 iPad 4 0 237.99 1
## 870 iPad 4 iPad 4 0 238.00 1
## 871 iPad 4 iPad 4 0 239.00 1
## 872 iPad 4 iPad 4 0 239.95 1
## 873 iPad 4 iPad 4 0 244.95 1
## 874 iPad 4 iPad 4 0 244.96 1
## 875 iPad 4 iPad 4 0 245.19 1
## 876 iPad 4 iPad 4 0 249.00 1
## 877 iPad 4 iPad 4 0 249.59 1
## 878 iPad 4 iPad 4 0 249.95 1
## 879 iPad 4 iPad 4 0 254.99 1
## 880 iPad 4 iPad 4 0 259.00 1
## 881 iPad 4 iPad 4 0 260.00 1
## 882 iPad 4 iPad 4 0 261.99 1
## 883 iPad 4 iPad 4 0 263.99 1
## 884 iPad 4 iPad 4 0 264.95 1
## 885 iPad 4 iPad 4 0 264.99 1
## 886 iPad 4 iPad 4 0 270.00 1
## 887 iPad 4 iPad 4 0 276.99 1
## 888 iPad 4 iPad 4 0 279.50 1
## 889 iPad 4 iPad 4 0 280.99 1
## 890 iPad 4 iPad 4 0 284.00 1
## 891 iPad 4 iPad 4 0 289.99 1
## 892 iPad 4 iPad 4 0 291.99 1
## 893 iPad 4 iPad 4 0 299.95 1
## 894 iPad 4 iPad 4 0 303.99 1
## 895 iPad 4 iPad 4 0 304.89 1
## 896 iPad 4 iPad 4 0 319.99 1
## 897 iPad 4 iPad 4 0 324.90 1
## 898 iPad 4 iPad 4 0 329.00 1
## 899 iPad 4 iPad 4 0 339.00 1
## 900 iPad 4 iPad 4 0 340.00 1
## 901 iPad 4 iPad 4 0 345.00 1
## 902 iPad 4 iPad 4 0 349.99 1
## 903 iPad 4 iPad 4 0 399.99 1
## 904 iPad 4 iPad 4 0 410.00 1
## 905 iPad 4 iPad 4 0 419.99 1
## 906 iPad 4 iPad 4 0 425.00 1
## 907 iPad 4 iPad 4 0 445.00 1
## 908 iPad 4 iPad 4 0 479.99 1
## 909 iPad 4 iPad 4 0 520.00 1
## 910 iPad 4 iPad 4 0 540.00 1
## 911 iPad 4 iPad 4 0 544.49 1
## 912 iPad 4 iPad 4 0 559.99 1
## 913 iPad 4 iPad 4 0 573.74 1
## 914 iPad 4 iPad 4 0 649.99 1
## 915 iPad 4 iPad 4 0 650.00 1
## 916 iPad 4 iPad 4 0 695.00 1
## 917 iPad 5 iPad 5 0 300.00 1
## 918 iPad Air iPad Air 0 20.00 1
## 919 iPad Air iPad Air 0 24.99 1
## 920 iPad Air iPad Air 0 25.00 1
## 921 iPad Air iPad Air 0 49.00 1
## 922 iPad Air iPad Air 0 50.00 1
## 923 iPad Air iPad Air 0 80.00 1
## 924 iPad Air iPad Air 0 99.00 1
## 925 iPad Air iPad Air 0 144.95 1
## 926 iPad Air iPad Air 0 149.00 1
## 927 iPad Air iPad Air 0 150.00 1
## 928 iPad Air iPad Air 0 160.00 1
## 929 iPad Air iPad Air 0 179.99 1
## 930 iPad Air iPad Air 0 184.99 1
## 931 iPad Air iPad Air 0 185.00 1
## 932 iPad Air iPad Air 0 187.00 1
## 933 iPad Air iPad Air 0 189.99 1
## 934 iPad Air iPad Air 0 225.00 1
## 935 iPad Air iPad Air 0 240.00 1
## 936 iPad Air iPad Air 0 242.00 1
## 937 iPad Air iPad Air 0 249.00 1
## 938 iPad Air iPad Air 0 249.99 1
## 939 iPad Air iPad Air 0 255.00 1
## 940 iPad Air iPad Air 0 256.24 1
## 941 iPad Air iPad Air 0 257.83 1
## 942 iPad Air iPad Air 0 259.99 1
## 943 iPad Air iPad Air 0 266.05 1
## 944 iPad Air iPad Air 0 269.85 1
## 945 iPad Air iPad Air 0 270.99 1
## 946 iPad Air iPad Air 0 274.00 1
## 947 iPad Air iPad Air 0 274.99 1
## 948 iPad Air iPad Air 0 275.00 1
## 949 iPad Air iPad Air 0 288.00 1
## 950 iPad Air iPad Air 0 289.79 1
## 951 iPad Air iPad Air 0 292.50 1
## 952 iPad Air iPad Air 0 294.99 1
## 953 iPad Air iPad Air 0 299.98 1
## 954 iPad Air iPad Air 0 310.00 1
## 955 iPad Air iPad Air 0 319.85 1
## 956 iPad Air iPad Air 0 322.99 1
## 957 iPad Air iPad Air 0 334.99 1
## 958 iPad Air iPad Air 0 339.99 1
## 959 iPad Air iPad Air 0 344.95 1
## 960 iPad Air iPad Air 0 346.00 1
## 961 iPad Air iPad Air 0 347.24 1
## 962 iPad Air iPad Air 0 349.95 1
## 963 iPad Air iPad Air 0 358.24 1
## 964 iPad Air iPad Air 0 359.99 1
## 965 iPad Air iPad Air 0 360.00 1
## 966 iPad Air iPad Air 0 360.24 1
## 967 iPad Air iPad Air 0 370.00 1
## 968 iPad Air iPad Air 0 374.95 1
## 969 iPad Air iPad Air 0 374.99 1
## 970 iPad Air iPad Air 0 375.99 1
## 971 iPad Air iPad Air 0 380.00 1
## 972 iPad Air iPad Air 0 384.99 1
## 973 iPad Air iPad Air 0 388.99 1
## 974 iPad Air iPad Air 0 389.00 1
## 975 iPad Air iPad Air 0 399.95 1
## 976 iPad Air iPad Air 0 404.99 1
## 977 iPad Air iPad Air 0 408.00 1
## 978 iPad Air iPad Air 0 420.00 1
## 979 iPad Air iPad Air 0 424.95 1
## 980 iPad Air iPad Air 0 429.99 1
## 981 iPad Air iPad Air 0 430.00 1
## 982 iPad Air iPad Air 0 438.00 1
## 983 iPad Air iPad Air 0 439.00 1
## 984 iPad Air iPad Air 0 439.99 1
## 985 iPad Air iPad Air 0 443.09 1
## 986 iPad Air iPad Air 0 455.00 1
## 987 iPad Air iPad Air 0 462.89 1
## 988 iPad Air iPad Air 0 469.99 1
## 989 iPad Air iPad Air 0 495.49 1
## 990 iPad Air iPad Air 0 509.99 1
## 991 iPad Air iPad Air 0 517.89 1
## 992 iPad Air iPad Air 0 539.95 1
## 993 iPad Air iPad Air 0 549.99 1
## 994 iPad Air iPad Air 0 550.00 1
## 995 iPad Air iPad Air 0 558.17 1
## 996 iPad Air iPad Air 0 565.95 1
## 997 iPad Air iPad Air 0 589.99 1
## 998 iPad Air iPad Air 0 599.99 1
## 999 iPad Air iPad Air 0 650.00 1
## 1000 iPad Air iPad Air 0 670.00 1
## 1001 iPad Air iPad Air 0 699.00 1
## 1002 iPad Air iPad Air 0 795.99 1
## 1003 iPad Air iPad Air 0 820.00 1
## 1004 iPad Air 2 iPad Air 2 0 0.01 1
## 1005 iPad Air 2 iPad Air 2 0 1.99 1
## 1006 iPad Air 2 iPad Air 2 0 9.00 1
## 1007 iPad Air 2 iPad Air 2 0 10.00 1
## 1008 iPad Air 2 iPad Air 2 0 59.00 1
## 1009 iPad Air 2 iPad Air 2 0 60.00 1
## 1010 iPad Air 2 iPad Air 2 0 99.95 1
## 1011 iPad Air 2 iPad Air 2 0 100.00 1
## 1012 iPad Air 2 iPad Air 2 0 139.00 1
## 1013 iPad Air 2 iPad Air 2 0 229.98 1
## 1014 iPad Air 2 iPad Air 2 0 295.00 1
## 1015 iPad Air 2 iPad Air 2 0 299.00 1
## 1016 iPad Air 2 iPad Air 2 0 299.99 1
## 1017 iPad Air 2 iPad Air 2 0 305.00 1
## 1018 iPad Air 2 iPad Air 2 0 310.00 1
## 1019 iPad Air 2 iPad Air 2 0 319.99 1
## 1020 iPad Air 2 iPad Air 2 0 320.00 1
## 1021 iPad Air 2 iPad Air 2 0 324.99 1
## 1022 iPad Air 2 iPad Air 2 0 339.00 1
## 1023 iPad Air 2 iPad Air 2 0 374.95 1
## 1024 iPad Air 2 iPad Air 2 0 375.00 1
## 1025 iPad Air 2 iPad Air 2 0 380.00 1
## 1026 iPad Air 2 iPad Air 2 0 389.99 1
## 1027 iPad Air 2 iPad Air 2 0 394.99 1
## 1028 iPad Air 2 iPad Air 2 0 395.00 1
## 1029 iPad Air 2 iPad Air 2 0 399.94 1
## 1030 iPad Air 2 iPad Air 2 0 399.95 1
## 1031 iPad Air 2 iPad Air 2 0 410.00 1
## 1032 iPad Air 2 iPad Air 2 0 424.55 1
## 1033 iPad Air 2 iPad Air 2 0 424.65 1
## 1034 iPad Air 2 iPad Air 2 0 424.99 1
## 1035 iPad Air 2 iPad Air 2 0 429.00 1
## 1036 iPad Air 2 iPad Air 2 0 429.95 1
## 1037 iPad Air 2 iPad Air 2 0 429.99 1
## 1038 iPad Air 2 iPad Air 2 0 430.00 1
## 1039 iPad Air 2 iPad Air 2 0 438.99 1
## 1040 iPad Air 2 iPad Air 2 0 439.98 1
## 1041 iPad Air 2 iPad Air 2 0 440.00 1
## 1042 iPad Air 2 iPad Air 2 0 444.99 1
## 1043 iPad Air 2 iPad Air 2 0 445.00 1
## 1044 iPad Air 2 iPad Air 2 0 454.00 1
## 1045 iPad Air 2 iPad Air 2 0 454.68 1
## 1046 iPad Air 2 iPad Air 2 0 459.00 1
## 1047 iPad Air 2 iPad Air 2 0 459.95 1
## 1048 iPad Air 2 iPad Air 2 0 459.99 1
## 1049 iPad Air 2 iPad Air 2 0 469.99 1
## 1050 iPad Air 2 iPad Air 2 0 485.00 1
## 1051 iPad Air 2 iPad Air 2 0 489.99 1
## 1052 iPad Air 2 iPad Air 2 0 490.00 1
## 1053 iPad Air 2 iPad Air 2 0 490.95 1
## 1054 iPad Air 2 iPad Air 2 0 495.99 1
## 1055 iPad Air 2 iPad Air 2 0 499.95 1
## 1056 iPad Air 2 iPad Air 2 0 509.00 1
## 1057 iPad Air 2 iPad Air 2 0 510.00 1
## 1058 iPad Air 2 iPad Air 2 0 514.95 1
## 1059 iPad Air 2 iPad Air 2 0 515.00 1
## 1060 iPad Air 2 iPad Air 2 0 520.00 1
## 1061 iPad Air 2 iPad Air 2 0 528.00 1
## 1062 iPad Air 2 iPad Air 2 0 529.00 1
## 1063 iPad Air 2 iPad Air 2 0 529.95 1
## 1064 iPad Air 2 iPad Air 2 0 529.99 1
## 1065 iPad Air 2 iPad Air 2 0 549.90 1
## 1066 iPad Air 2 iPad Air 2 0 549.95 1
## 1067 iPad Air 2 iPad Air 2 0 559.00 1
## 1068 iPad Air 2 iPad Air 2 0 579.99 1
## 1069 iPad Air 2 iPad Air 2 0 585.99 1
## 1070 iPad Air 2 iPad Air 2 0 589.00 1
## 1071 iPad Air 2 iPad Air 2 0 590.00 1
## 1072 iPad Air 2 iPad Air 2 0 595.00 1
## 1073 iPad Air 2 iPad Air 2 0 598.98 1
## 1074 iPad Air 2 iPad Air 2 0 600.00 1
## 1075 iPad Air 2 iPad Air 2 0 614.99 1
## 1076 iPad Air 2 iPad Air 2 0 615.99 1
## 1077 iPad Air 2 iPad Air 2 0 619.00 1
## 1078 iPad Air 2 iPad Air 2 0 619.99 1
## 1079 iPad Air 2 iPad Air 2 0 624.99 1
## 1080 iPad Air 2 iPad Air 2 0 625.00 1
## 1081 iPad Air 2 iPad Air 2 0 629.00 1
## 1082 iPad Air 2 iPad Air 2 0 630.00 1
## 1083 iPad Air 2 iPad Air 2 0 634.99 1
## 1084 iPad Air 2 iPad Air 2 0 645.00 1
## 1085 iPad Air 2 iPad Air 2 0 645.99 1
## 1086 iPad Air 2 iPad Air 2 0 649.95 1
## 1087 iPad Air 2 iPad Air 2 0 649.99 1
## 1088 iPad Air 2 iPad Air 2 0 659.49 1
## 1089 iPad Air 2 iPad Air 2 0 660.00 1
## 1090 iPad Air 2 iPad Air 2 0 675.00 1
## 1091 iPad Air 2 iPad Air 2 0 679.95 1
## 1092 iPad Air 2 iPad Air 2 0 679.99 1
## 1093 iPad Air 2 iPad Air 2 0 680.00 1
## 1094 iPad Air 2 iPad Air 2 0 710.00 1
## 1095 iPad Air 2 iPad Air 2 0 730.00 1
## 1096 iPad Air 2 iPad Air 2 0 740.00 1
## 1097 iPad Air 2 iPad Air 2 0 749.99 1
## 1098 iPad Air 2 iPad Air 2 0 785.00 1
## 1099 iPad Air 2 iPad Air 2 0 789.00 1
## 1100 iPad Air 2 iPad Air 2 0 789.99 1
## 1101 iPad Air 2 iPad Air 2 0 795.00 1
## 1102 iPad Air 2 iPad Air 2 0 798.00 1
## 1103 iPad Air 2 iPad Air 2 0 799.00 1
## 1104 iPad Air 2 iPad Air 2 0 829.99 1
## 1105 iPad Air 2 iPad Air 2 0 879.99 1
## 1106 iPad Air 2 iPad Air 2 0 899.99 1
## 1107 iPad Air 2 iPad Air 2 0 900.00 1
## 1108 iPad Air 2 iPad Air 2 0 939.00 1
## 1109 iPad mini Unknown 1 190.00 1
## 1110 iPad mini Unknown 1 409.99 1
## 1111 iPad mini Unknown 1 999.99 1
## 1112 iPad mini iPad mini 0 0.98 1
## 1113 iPad mini iPad mini 0 9.99 1
## 1114 iPad mini iPad mini 0 10.99 1
## 1115 iPad mini iPad mini 0 19.50 1
## 1116 iPad mini iPad mini 0 19.99 1
## 1117 iPad mini iPad mini 0 29.99 1
## 1118 iPad mini iPad mini 0 40.00 1
## 1119 iPad mini iPad mini 0 42.00 1
## 1120 iPad mini iPad mini 0 49.95 1
## 1121 iPad mini iPad mini 0 59.99 1
## 1122 iPad mini iPad mini 0 62.00 1
## 1123 iPad mini iPad mini 0 74.95 1
## 1124 iPad mini iPad mini 0 74.99 1
## 1125 iPad mini iPad mini 0 79.00 1
## 1126 iPad mini iPad mini 0 79.99 1
## 1127 iPad mini iPad mini 0 84.99 1
## 1128 iPad mini iPad mini 0 89.00 1
## 1129 iPad mini iPad mini 0 109.00 1
## 1130 iPad mini iPad mini 0 109.99 1
## 1131 iPad mini iPad mini 0 110.00 1
## 1132 iPad mini iPad mini 0 112.00 1
## 1133 iPad mini iPad mini 0 113.00 1
## 1134 iPad mini iPad mini 0 118.00 1
## 1135 iPad mini iPad mini 0 119.98 1
## 1136 iPad mini iPad mini 0 129.00 1
## 1137 iPad mini iPad mini 0 129.95 1
## 1138 iPad mini iPad mini 0 129.99 1
## 1139 iPad mini iPad mini 0 135.00 1
## 1140 iPad mini iPad mini 0 139.00 1
## 1141 iPad mini iPad mini 0 140.00 1
## 1142 iPad mini iPad mini 0 144.99 1
## 1143 iPad mini iPad mini 0 145.00 1
## 1144 iPad mini iPad mini 0 149.59 1
## 1145 iPad mini iPad mini 0 149.95 1
## 1146 iPad mini iPad mini 0 149.99 1
## 1147 iPad mini iPad mini 0 159.95 1
## 1148 iPad mini iPad mini 0 160.57 1
## 1149 iPad mini iPad mini 0 168.00 1
## 1150 iPad mini iPad mini 0 170.00 1
## 1151 iPad mini iPad mini 0 171.95 1
## 1152 iPad mini iPad mini 0 176.27 1
## 1153 iPad mini iPad mini 0 178.99 1
## 1154 iPad mini iPad mini 0 179.00 1
## 1155 iPad mini iPad mini 0 179.96 1
## 1156 iPad mini iPad mini 0 180.00 1
## 1157 iPad mini iPad mini 0 181.00 1
## 1158 iPad mini iPad mini 0 184.99 1
## 1159 iPad mini iPad mini 0 185.00 1
## 1160 iPad mini iPad mini 0 185.49 1
## 1161 iPad mini iPad mini 0 187.89 1
## 1162 iPad mini iPad mini 0 188.88 1
## 1163 iPad mini iPad mini 0 190.00 1
## 1164 iPad mini iPad mini 0 194.29 1
## 1165 iPad mini iPad mini 0 195.00 1
## 1166 iPad mini iPad mini 0 198.00 1
## 1167 iPad mini iPad mini 0 199.97 1
## 1168 iPad mini iPad mini 0 205.00 1
## 1169 iPad mini iPad mini 0 208.00 1
## 1170 iPad mini iPad mini 0 208.99 1
## 1171 iPad mini iPad mini 0 209.00 1
## 1172 iPad mini iPad mini 0 209.85 1
## 1173 iPad mini iPad mini 0 209.99 1
## 1174 iPad mini iPad mini 0 211.50 1
## 1175 iPad mini iPad mini 0 212.99 1
## 1176 iPad mini iPad mini 0 214.98 1
## 1177 iPad mini iPad mini 0 215.99 1
## 1178 iPad mini iPad mini 0 219.00 1
## 1179 iPad mini iPad mini 0 220.00 1
## 1180 iPad mini iPad mini 0 227.88 1
## 1181 iPad mini iPad mini 0 235.00 1
## 1182 iPad mini iPad mini 0 239.00 1
## 1183 iPad mini iPad mini 0 240.00 1
## 1184 iPad mini iPad mini 0 241.88 1
## 1185 iPad mini iPad mini 0 244.97 1
## 1186 iPad mini iPad mini 0 249.95 1
## 1187 iPad mini iPad mini 0 252.88 1
## 1188 iPad mini iPad mini 0 255.00 1
## 1189 iPad mini iPad mini 0 258.88 1
## 1190 iPad mini iPad mini 0 259.00 1
## 1191 iPad mini iPad mini 0 260.00 1
## 1192 iPad mini iPad mini 0 265.00 1
## 1193 iPad mini iPad mini 0 265.99 1
## 1194 iPad mini iPad mini 0 271.00 1
## 1195 iPad mini iPad mini 0 279.00 1
## 1196 iPad mini iPad mini 0 279.50 1
## 1197 iPad mini iPad mini 0 279.99 1
## 1198 iPad mini iPad mini 0 289.00 1
## 1199 iPad mini iPad mini 0 289.99 1
## 1200 iPad mini iPad mini 0 295.00 1
## 1201 iPad mini iPad mini 0 298.00 1
## 1202 iPad mini iPad mini 0 299.95 1
## 1203 iPad mini iPad mini 0 310.00 1
## 1204 iPad mini iPad mini 0 315.00 1
## 1205 iPad mini iPad mini 0 320.00 1
## 1206 iPad mini iPad mini 0 334.95 1
## 1207 iPad mini iPad mini 0 339.99 1
## 1208 iPad mini iPad mini 0 348.60 1
## 1209 iPad mini iPad mini 0 349.99 1
## 1210 iPad mini iPad mini 0 351.00 1
## 1211 iPad mini iPad mini 0 358.87 1
## 1212 iPad mini iPad mini 0 370.00 1
## 1213 iPad mini iPad mini 0 375.00 1
## 1214 iPad mini iPad mini 0 379.99 1
## 1215 iPad mini iPad mini 0 385.00 1
## 1216 iPad mini iPad mini 0 387.45 1
## 1217 iPad mini iPad mini 0 388.30 1
## 1218 iPad mini iPad mini 0 397.75 1
## 1219 iPad mini iPad mini 0 398.99 1
## 1220 iPad mini iPad mini 0 399.99 1
## 1221 iPad mini iPad mini 0 429.99 1
## 1222 iPad mini iPad mini 0 475.00 1
## 1223 iPad mini iPad mini 0 499.99 1
## 1224 iPad mini iPad mini 0 720.12 1
## 1225 iPad mini iPad mini 0 999.00 1
## 1226 iPad mini iPad mini 1 9.99 1
## 1227 iPad mini iPad mini 1 49.99 1
## 1228 iPad mini iPad mini 1 100.00 1
## 1229 iPad mini iPad mini 1 149.00 1
## 1230 iPad mini iPad mini 1 169.99 1
## 1231 iPad mini iPad mini 1 249.99 1
## 1232 iPad mini iPad mini 1 429.00 1
## 1233 iPad mini iPad mini 2 99.99 1
## 1234 iPad mini 2 iPad mini 2 0 0.01 1
## 1235 iPad mini 2 iPad mini 2 0 10.00 1
## 1236 iPad mini 2 iPad mini 2 0 25.00 1
## 1237 iPad mini 2 iPad mini 2 0 49.99 1
## 1238 iPad mini 2 iPad mini 2 0 79.95 1
## 1239 iPad mini 2 iPad mini 2 0 99.97 1
## 1240 iPad mini 2 iPad mini 2 0 119.00 1
## 1241 iPad mini 2 iPad mini 2 0 129.99 1
## 1242 iPad mini 2 iPad mini 2 0 130.00 1
## 1243 iPad mini 2 iPad mini 2 0 145.00 1
## 1244 iPad mini 2 iPad mini 2 0 149.00 1
## 1245 iPad mini 2 iPad mini 2 0 149.95 1
## 1246 iPad mini 2 iPad mini 2 0 150.00 1
## 1247 iPad mini 2 iPad mini 2 0 155.00 1
## 1248 iPad mini 2 iPad mini 2 0 160.00 1
## 1249 iPad mini 2 iPad mini 2 0 185.00 1
## 1250 iPad mini 2 iPad mini 2 0 199.00 1
## 1251 iPad mini 2 iPad mini 2 0 209.98 1
## 1252 iPad mini 2 iPad mini 2 0 210.00 1
## 1253 iPad mini 2 iPad mini 2 0 215.00 1
## 1254 iPad mini 2 iPad mini 2 0 217.00 1
## 1255 iPad mini 2 iPad mini 2 0 222.72 1
## 1256 iPad mini 2 iPad mini 2 0 223.00 1
## 1257 iPad mini 2 iPad mini 2 0 229.00 1
## 1258 iPad mini 2 iPad mini 2 0 237.00 1
## 1259 iPad mini 2 iPad mini 2 0 239.00 1
## 1260 iPad mini 2 iPad mini 2 0 239.99 1
## 1261 iPad mini 2 iPad mini 2 0 245.00 1
## 1262 iPad mini 2 iPad mini 2 0 248.18 1
## 1263 iPad mini 2 iPad mini 2 0 249.00 1
## 1264 iPad mini 2 iPad mini 2 0 259.95 1
## 1265 iPad mini 2 iPad mini 2 0 260.00 1
## 1266 iPad mini 2 iPad mini 2 0 264.99 1
## 1267 iPad mini 2 iPad mini 2 0 279.99 1
## 1268 iPad mini 2 iPad mini 2 0 289.95 1
## 1269 iPad mini 2 iPad mini 2 0 295.00 1
## 1270 iPad mini 2 iPad mini 2 0 299.99 1
## 1271 iPad mini 2 iPad mini 2 0 308.00 1
## 1272 iPad mini 2 iPad mini 2 0 310.00 1
## 1273 iPad mini 2 iPad mini 2 0 319.98 1
## 1274 iPad mini 2 iPad mini 2 0 319.99 1
## 1275 iPad mini 2 iPad mini 2 0 327.58 1
## 1276 iPad mini 2 iPad mini 2 0 339.00 1
## 1277 iPad mini 2 iPad mini 2 0 339.99 1
## 1278 iPad mini 2 iPad mini 2 0 376.00 1
## 1279 iPad mini 2 iPad mini 2 0 379.99 1
## 1280 iPad mini 2 iPad mini 2 0 380.00 1
## 1281 iPad mini 2 iPad mini 2 0 385.00 1
## 1282 iPad mini 2 iPad mini 2 0 387.00 1
## 1283 iPad mini 2 iPad mini 2 0 395.00 1
## 1284 iPad mini 2 iPad mini 2 0 400.00 1
## 1285 iPad mini 2 iPad mini 2 0 429.99 1
## 1286 iPad mini 2 iPad mini 2 0 430.00 1
## 1287 iPad mini 2 iPad mini 2 0 449.00 1
## 1288 iPad mini 2 iPad mini 2 0 450.00 1
## 1289 iPad mini 2 iPad mini 2 0 458.00 1
## 1290 iPad mini 2 iPad mini 2 0 460.00 1
## 1291 iPad mini 2 iPad mini 2 0 469.00 1
## 1292 iPad mini 2 iPad mini 2 0 500.00 1
## 1293 iPad mini 2 iPad mini 2 0 509.00 1
## 1294 iPad mini 2 iPad mini 2 0 550.00 1
## 1295 iPad mini 2 iPad mini 2 0 575.00 1
## 1296 iPad mini 2 iPad mini 2 0 595.00 1
## 1297 iPad mini 2 iPad mini 2 1 195.00 1
## 1298 iPad mini 2 iPad mini 2 1 201.99 1
## 1299 iPad mini 2 iPad mini 2 1 225.00 1
## 1300 iPad mini 2 iPad mini 2 1 238.80 1
## 1301 iPad mini 2 iPad mini 2 1 249.00 1
## 1302 iPad mini 2 iPad mini 2 1 300.00 1
## 1303 iPad mini 2 iPad mini 2 1 350.25 1
## 1304 iPad mini 3 iPad mini 3 0 0.45 1
## 1305 iPad mini 3 iPad mini 3 0 9.95 1
## 1306 iPad mini 3 iPad mini 3 0 25.00 1
## 1307 iPad mini 3 iPad mini 3 0 100.00 1
## 1308 iPad mini 3 iPad mini 3 0 149.00 1
## 1309 iPad mini 3 iPad mini 3 0 175.00 1
## 1310 iPad mini 3 iPad mini 3 0 197.97 1
## 1311 iPad mini 3 iPad mini 3 0 199.99 1
## 1312 iPad mini 3 iPad mini 3 0 249.00 1
## 1313 iPad mini 3 iPad mini 3 0 250.00 1
## 1314 iPad mini 3 iPad mini 3 0 290.00 1
## 1315 iPad mini 3 iPad mini 3 0 295.95 1
## 1316 iPad mini 3 iPad mini 3 0 299.00 1
## 1317 iPad mini 3 iPad mini 3 0 309.95 1
## 1318 iPad mini 3 iPad mini 3 0 329.00 1
## 1319 iPad mini 3 iPad mini 3 0 331.99 1
## 1320 iPad mini 3 iPad mini 3 0 332.50 1
## 1321 iPad mini 3 iPad mini 3 0 334.00 1
## 1322 iPad mini 3 iPad mini 3 0 335.00 1
## 1323 iPad mini 3 iPad mini 3 0 339.50 1
## 1324 iPad mini 3 iPad mini 3 0 339.98 1
## 1325 iPad mini 3 iPad mini 3 0 340.00 1
## 1326 iPad mini 3 iPad mini 3 0 349.95 1
## 1327 iPad mini 3 iPad mini 3 0 349.99 1
## 1328 iPad mini 3 iPad mini 3 0 359.00 1
## 1329 iPad mini 3 iPad mini 3 0 359.99 1
## 1330 iPad mini 3 iPad mini 3 0 370.00 1
## 1331 iPad mini 3 iPad mini 3 0 379.95 1
## 1332 iPad mini 3 iPad mini 3 0 379.99 1
## 1333 iPad mini 3 iPad mini 3 0 380.00 1
## 1334 iPad mini 3 iPad mini 3 0 385.00 1
## 1335 iPad mini 3 iPad mini 3 0 394.99 1
## 1336 iPad mini 3 iPad mini 3 0 399.00 1
## 1337 iPad mini 3 iPad mini 3 0 419.95 1
## 1338 iPad mini 3 iPad mini 3 0 425.00 1
## 1339 iPad mini 3 iPad mini 3 0 426.99 1
## 1340 iPad mini 3 iPad mini 3 0 439.99 1
## 1341 iPad mini 3 iPad mini 3 0 445.95 1
## 1342 iPad mini 3 iPad mini 3 0 449.95 1
## 1343 iPad mini 3 iPad mini 3 0 450.00 1
## 1344 iPad mini 3 iPad mini 3 0 459.99 1
## 1345 iPad mini 3 iPad mini 3 0 469.99 1
## 1346 iPad mini 3 iPad mini 3 0 475.00 1
## 1347 iPad mini 3 iPad mini 3 0 485.00 1
## 1348 iPad mini 3 iPad mini 3 0 510.00 1
## 1349 iPad mini 3 iPad mini 3 0 525.00 1
## 1350 iPad mini 3 iPad mini 3 0 529.99 1
## 1351 iPad mini 3 iPad mini 3 0 549.99 1
## 1352 iPad mini 3 iPad mini 3 0 550.00 1
## 1353 iPad mini 3 iPad mini 3 0 559.99 1
## 1354 iPad mini 3 iPad mini 3 0 569.00 1
## 1355 iPad mini 3 iPad mini 3 0 575.00 1
## 1356 iPad mini 3 iPad mini 3 0 579.99 1
## 1357 iPad mini 3 iPad mini 3 0 609.99 1
## 1358 iPad mini 3 iPad mini 3 0 614.99 1
## 1359 iPad mini 3 iPad mini 3 0 639.99 1
## 1360 iPad mini 3 iPad mini 3 0 650.00 1
## 1361 iPad mini 3 iPad mini 3 0 689.99 1
## 1362 iPad mini 3 iPad mini 3 0 799.99 1
## 1363 iPad mini 3 iPad mini 3 0 948.98 1
## 1364 iPad mini 3 iPad mini 3 1 400.00 1
## 1365 iPad mini 3 iPad mini 3 1 419.99 1
## 1366 iPad mini 3 iPad mini 3 1 460.00 1
## 1367 iPad mini 3 iPad mini 3 1 499.99 1
## 1368 iPad mini 3 iPad mini 3 1 599.99 1
## 1369 iPad mini Retina iPad mini Retina 0 160.00 1
## 1370 iPad mini Retina iPad mini Retina 0 235.00 1
## 1371 iPad mini Retina iPad mini Retina 0 250.00 1
## 1372 iPad mini Retina iPad mini Retina 0 299.00 1
## 1373 iPad mini Retina iPad mini Retina 0 339.00 1
## 1374 iPad mini Retina iPad mini Retina 0 350.00 1
## 1375 iPad mini Retina iPad mini Retina 0 420.00 1
## 1376 iPad mini Retina iPad mini Retina 1 303.67 1glb_allobs_df[glb_allobs_df$UniqueID == 11863, "D.P.air"] <- 0
glb_allobs_df[(glb_allobs_df$D.P.air == 1) & (glb_allobs_df$productline == "Unknown"), "prdline.my"] <- "iPad Air"
print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.air", glb_rsp_var)))## prdline.my productline D.P.air startprice .n
## 1 iPad 2 iPad 2 0 0.99 38
## 2 iPad mini iPad mini 0 0.99 34
## 3 iPad 1 iPad 1 0 0.99 26
## 4 Unknown Unknown 0 0.99 25
## 5 iPad 1 iPad 1 0 50.00 22
## 6 iPad mini iPad mini 0 150.00 20
## 7 iPad Air iPad Air 0 0.99 17
## 8 iPad 2 iPad 2 0 150.00 16
## 9 iPad 4 iPad 4 0 0.99 15
## 10 iPad mini iPad mini 0 100.00 15
## 11 iPad 2 iPad 2 0 100.00 13
## 12 iPad Air 2 iPad Air 2 0 0.99 13
## 13 iPad mini 2 iPad mini 2 0 0.99 13
## 14 iPad 1 iPad 1 0 80.00 12
## 15 iPad 3 iPad 3 0 0.99 12
## 16 iPad 3 iPad 3 0 200.00 12
## 17 iPad 1 iPad 1 0 90.00 11
## 18 iPad 2 iPad 2 0 175.00 11
## 19 iPad mini iPad mini 0 99.99 11
## 20 Unknown Unknown 0 150.00 10
## 21 iPad 1 iPad 1 0 75.00 10
## 22 iPad 1 iPad 1 0 100.00 10
## 23 iPad 2 iPad 2 0 0.01 10
## 24 iPad 3 iPad 3 0 250.00 10
## 25 iPad mini iPad mini 0 50.00 10
## 26 Unknown Unknown 0 100.00 9
## 27 iPad 2 iPad 2 0 99.99 9
## 28 iPad 2 iPad 2 0 149.99 9
## 29 iPad 2 iPad 2 0 199.99 9
## 30 iPad Air iPad Air 0 300.00 9
## 31 iPad mini iPad mini 0 199.99 9
## 32 Unknown Unknown 0 300.00 8
## 33 iPad 1 iPad 1 0 95.00 8
## 34 iPad 2 iPad 2 0 99.00 8
## 35 iPad 2 iPad 2 0 125.00 8
## 36 iPad 2 iPad 2 0 200.00 8
## 37 iPad 4 iPad 4 0 249.99 8
## 38 iPad Air 2 iPad Air 2 0 550.00 8
## 39 iPad mini iPad mini 0 200.00 8
## 40 iPad mini 2 iPad mini 2 0 350.00 8
## 41 Unknown Unknown 0 50.00 7
## 42 iPad 1 iPad 1 0 70.00 7
## 43 iPad 2 iPad 2 0 9.99 7
## 44 iPad 2 iPad 2 0 75.00 7
## 45 iPad 2 iPad 2 0 180.00 7
## 46 iPad 4 iPad 4 0 199.99 7
## 47 iPad mini iPad mini 0 99.00 7
## 48 iPad mini 3 iPad mini 3 0 0.99 7
## 49 iPad 1 iPad 1 0 1.00 6
## 50 iPad 2 iPad 2 0 50.00 6
## 51 iPad 2 iPad 2 0 160.00 6
## 52 iPad 4 iPad 4 0 100.00 6
## 53 iPad 4 iPad 4 0 150.00 6
## 54 iPad Air iPad Air 0 1.00 6
## 55 iPad Air iPad Air 0 200.00 6
## 56 iPad Air iPad Air 0 400.00 6
## 57 iPad Air 2 iPad Air 2 0 450.00 6
## 58 iPad mini iPad mini 0 75.00 6
## 59 iPad mini iPad mini 0 89.99 6
## 60 iPad mini iPad mini 0 159.99 6
## 61 iPad mini iPad mini 0 175.00 6
## 62 iPad mini iPad mini 0 199.00 6
## 63 iPad mini 2 iPad mini 2 0 225.00 6
## 64 iPad 1 iPad 1 0 29.99 5
## 65 iPad 1 iPad 1 0 55.00 5
## 66 iPad 1 iPad 1 0 79.99 5
## 67 iPad 1 iPad 1 0 99.00 5
## 68 iPad 2 iPad 2 0 80.00 5
## 69 iPad 2 iPad 2 0 165.00 5
## 70 iPad 2 iPad 2 0 179.00 5
## 71 iPad 3 iPad 3 0 99.00 5
## 72 iPad 3 iPad 3 0 150.00 5
## 73 iPad 3 iPad 3 0 220.00 5
## 74 iPad 3 iPad 3 0 225.00 5
## 75 iPad 3 iPad 3 0 300.00 5
## 76 iPad 4 iPad 4 0 250.00 5
## 77 iPad 4 iPad 4 0 279.99 5
## 78 iPad 4 iPad 4 0 400.00 5
## 79 iPad Air iPad Air 0 100.00 5
## 80 iPad Air iPad Air 0 250.00 5
## 81 iPad Air iPad Air 0 350.00 5
## 82 iPad Air iPad Air 0 389.99 5
## 83 iPad Air 2 iPad Air 2 0 499.99 5
## 84 iPad mini iPad mini 0 1.00 5
## 85 iPad mini iPad mini 0 250.00 5
## 86 iPad mini iPad mini 0 350.00 5
## 87 iPad mini 2 iPad mini 2 0 200.00 5
## 88 iPad mini 3 iPad mini 3 0 499.99 5
## 89 iPad mini 3 iPad mini 3 0 599.99 5
## 90 Unknown Unknown 0 25.00 4
## 91 Unknown Unknown 0 149.99 4
## 92 Unknown Unknown 0 250.00 4
## 93 iPad 1 iPad 1 0 40.00 4
## 94 iPad 1 iPad 1 0 49.99 4
## 95 iPad 1 iPad 1 0 79.00 4
## 96 iPad 1 iPad 1 0 105.00 4
## 97 iPad 1 iPad 1 0 110.00 4
## 98 iPad 2 iPad 2 0 1.00 4
## 99 iPad 2 iPad 2 0 40.00 4
## 100 iPad 2 iPad 2 0 49.99 4
## 101 iPad 2 iPad 2 0 130.00 4
## 102 iPad 2 iPad 2 0 140.00 4
## 103 iPad 2 iPad 2 0 155.00 4
## 104 iPad 2 iPad 2 0 164.99 4
## 105 iPad 2 iPad 2 0 174.99 4
## 106 iPad 2 iPad 2 0 179.99 4
## 107 iPad 2 iPad 2 0 189.99 4
## 108 iPad 2 iPad 2 0 250.00 4
## 109 iPad 3 iPad 3 0 100.00 4
## 110 iPad 3 iPad 3 0 149.99 4
## 111 iPad 3 iPad 3 0 175.00 4
## 112 iPad 3 iPad 3 0 199.99 4
## 113 iPad 3 iPad 3 0 219.99 4
## 114 iPad 3 iPad 3 0 249.99 4
## 115 iPad 3 iPad 3 0 275.00 4
## 116 iPad 4 iPad 4 0 0.01 4
## 117 iPad 4 iPad 4 0 99.99 4
## 118 iPad 4 iPad 4 0 200.00 4
## 119 iPad 4 iPad 4 0 299.00 4
## 120 iPad Air iPad Air 0 279.99 4
## 121 iPad Air iPad Air 0 325.00 4
## 122 iPad Air iPad Air 0 329.99 4
## 123 iPad Air iPad Air 0 500.00 4
## 124 iPad Air 2 iPad Air 2 0 250.00 4
## 125 iPad Air 2 iPad Air 2 0 350.00 4
## 126 iPad Air 2 iPad Air 2 0 399.00 4
## 127 iPad Air 2 iPad Air 2 0 399.99 4
## 128 iPad Air 2 iPad Air 2 0 400.00 4
## 129 iPad Air 2 iPad Air 2 0 500.00 4
## 130 iPad Air 2 iPad Air 2 0 549.99 4
## 131 iPad mini iPad mini 0 119.99 4
## 132 iPad mini iPad mini 0 130.00 4
## 133 iPad mini iPad mini 0 149.00 4
## 134 iPad mini iPad mini 0 249.99 4
## 135 iPad mini iPad mini 0 275.00 4
## 136 iPad mini iPad mini 0 300.00 4
## 137 iPad mini 2 iPad mini 2 0 175.00 4
## 138 iPad mini 2 iPad mini 2 0 250.00 4
## 139 iPad mini 2 iPad mini 2 0 300.00 4
## 140 iPad mini 3 iPad mini 3 0 325.00 4
## 141 iPad mini 3 iPad mini 3 0 400.00 4
## 142 Unknown Unknown 0 15.00 3
## 143 Unknown Unknown 0 40.00 3
## 144 Unknown Unknown 0 75.00 3
## 145 Unknown Unknown 0 99.00 3
## 146 Unknown Unknown 0 120.00 3
## 147 Unknown Unknown 0 199.00 3
## 148 Unknown Unknown 0 199.99 3
## 149 Unknown Unknown 0 200.00 3
## 150 Unknown Unknown 0 249.00 3
## 151 Unknown Unknown 0 249.99 3
## 152 Unknown Unknown 0 299.99 3
## 153 Unknown Unknown 0 319.00 3
## 154 Unknown Unknown 0 350.00 3
## 155 iPad 1 iPad 1 0 0.01 3
## 156 iPad 1 iPad 1 0 19.99 3
## 157 iPad 1 iPad 1 0 20.00 3
## 158 iPad 1 iPad 1 0 25.00 3
## 159 iPad 1 iPad 1 0 30.00 3
## 160 iPad 1 iPad 1 0 36.95 3
## 161 iPad 1 iPad 1 0 65.00 3
## 162 iPad 1 iPad 1 0 84.99 3
## 163 iPad 1 iPad 1 0 85.00 3
## 164 iPad 1 iPad 1 0 89.00 3
## 165 iPad 1 iPad 1 0 99.99 3
## 166 iPad 1 iPad 1 0 119.99 3
## 167 iPad 1 iPad 1 0 150.00 3
## 168 iPad 1 iPad 1 0 180.00 3
## 169 iPad 2 iPad 2 0 30.00 3
## 170 iPad 2 iPad 2 0 70.00 3
## 171 iPad 2 iPad 2 0 85.00 3
## 172 iPad 2 iPad 2 0 89.99 3
## 173 iPad 2 iPad 2 0 90.00 3
## 174 iPad 2 iPad 2 0 120.00 3
## 175 iPad 2 iPad 2 0 129.95 3
## 176 iPad 2 iPad 2 0 129.99 3
## 177 iPad 2 iPad 2 0 139.00 3
## 178 iPad 2 iPad 2 0 149.00 3
## 179 iPad 2 iPad 2 0 149.95 3
## 180 iPad 2 iPad 2 0 154.00 3
## 181 iPad 2 iPad 2 0 159.99 3
## 182 iPad 2 iPad 2 0 169.00 3
## 183 iPad 2 iPad 2 0 249.97 3
## 184 iPad 2 iPad 2 0 275.00 3
## 185 iPad 2 iPad 2 0 300.00 3
## 186 iPad 3 iPad 3 0 1.00 3
## 187 iPad 3 iPad 3 0 9.99 3
## 188 iPad 3 iPad 3 0 10.00 3
## 189 iPad 3 iPad 3 0 99.99 3
## 190 iPad 3 iPad 3 0 128.00 3
## 191 iPad 3 iPad 3 0 185.00 3
## 192 iPad 3 iPad 3 0 187.50 3
## 193 iPad 3 iPad 3 0 199.00 3
## 194 iPad 4 iPad 4 0 50.00 3
## 195 iPad 4 iPad 4 0 225.00 3
## 196 iPad 4 iPad 4 0 259.99 3
## 197 iPad 4 iPad 4 0 275.00 3
## 198 iPad 4 iPad 4 0 280.00 3
## 199 iPad 4 iPad 4 0 300.00 3
## 200 iPad 4 iPad 4 0 320.00 3
## 201 iPad Air iPad Air 0 90.00 3
## 202 iPad Air iPad Air 0 199.99 3
## 203 iPad Air iPad Air 0 229.00 3
## 204 iPad Air iPad Air 0 299.99 3
## 205 iPad Air iPad Air 0 320.00 3
## 206 iPad Air iPad Air 0 379.00 3
## 207 iPad Air iPad Air 0 415.00 3
## 208 iPad Air 2 iPad Air 2 0 1.00 3
## 209 iPad Air 2 iPad Air 2 0 50.00 3
## 210 iPad Air 2 iPad Air 2 0 199.99 3
## 211 iPad Air 2 iPad Air 2 0 425.00 3
## 212 iPad Air 2 iPad Air 2 0 439.99 3
## 213 iPad Air 2 iPad Air 2 0 480.00 3
## 214 iPad Air 2 iPad Air 2 0 499.00 3
## 215 iPad Air 2 iPad Air 2 0 525.00 3
## 216 iPad Air 2 iPad Air 2 0 560.00 3
## 217 iPad mini iPad mini 0 0.01 3
## 218 iPad mini iPad mini 0 20.00 3
## 219 iPad mini iPad mini 0 25.00 3
## 220 iPad mini iPad mini 0 45.00 3
## 221 iPad mini iPad mini 0 60.00 3
## 222 iPad mini iPad mini 0 125.00 3
## 223 iPad mini iPad mini 0 160.00 3
## 224 iPad mini iPad mini 0 179.00 3
## 225 iPad mini iPad mini 0 179.99 3
## 226 iPad mini iPad mini 0 189.99 3
## 227 iPad mini iPad mini 0 210.00 3
## 228 iPad mini iPad mini 0 259.99 3
## 229 iPad mini iPad mini 0 290.00 3
## 230 iPad mini iPad mini 0 400.00 3
## 231 iPad mini 2 iPad mini 2 0 100.00 3
## 232 iPad mini 2 iPad mini 2 0 120.00 3
## 233 iPad mini 2 iPad mini 2 0 180.00 3
## 234 iPad mini 2 iPad mini 2 0 285.00 3
## 235 iPad mini 2 iPad mini 2 0 375.00 3
## 236 iPad mini 3 iPad mini 3 0 99.00 3
## 237 iPad mini 3 iPad mini 3 0 300.00 3
## 238 iPad mini 3 iPad mini 3 0 329.99 3
## 239 iPad mini 3 iPad mini 3 0 350.00 3
## 240 iPad mini 3 iPad mini 3 0 399.99 3
## 241 iPad mini 3 iPad mini 3 0 449.99 3
## 242 iPad mini 3 iPad mini 3 0 729.99 3
## 243 Unknown Unknown 0 5.00 2
## 244 Unknown Unknown 0 9.99 2
## 245 Unknown Unknown 0 19.99 2
## 246 Unknown Unknown 0 20.00 2
## 247 Unknown Unknown 0 39.99 2
## 248 Unknown Unknown 0 70.00 2
## 249 Unknown Unknown 0 79.95 2
## 250 Unknown Unknown 0 80.00 2
## 251 Unknown Unknown 0 99.99 2
## 252 Unknown Unknown 0 108.00 2
## 253 Unknown Unknown 0 159.99 2
## 254 Unknown Unknown 0 165.00 2
## 255 Unknown Unknown 0 169.99 2
## 256 Unknown Unknown 0 175.00 2
## 257 Unknown Unknown 0 185.00 2
## 258 Unknown Unknown 0 280.00 2
## 259 Unknown Unknown 0 319.99 2
## 260 Unknown Unknown 0 375.00 2
## 261 Unknown Unknown 0 399.00 2
## 262 Unknown Unknown 0 450.00 2
## 263 Unknown Unknown 0 500.00 2
## 264 Unknown Unknown 0 599.99 2
## 265 Unknown Unknown 0 700.00 2
## 266 iPad 1 iPad 1 0 9.50 2
## 267 iPad 1 iPad 1 0 9.99 2
## 268 iPad 1 iPad 1 0 10.00 2
## 269 iPad 1 iPad 1 0 14.99 2
## 270 iPad 1 iPad 1 0 15.00 2
## 271 iPad 1 iPad 1 0 45.00 2
## 272 iPad 1 iPad 1 0 58.00 2
## 273 iPad 1 iPad 1 0 60.00 2
## 274 iPad 1 iPad 1 0 62.00 2
## 275 iPad 1 iPad 1 0 69.00 2
## 276 iPad 1 iPad 1 0 69.99 2
## 277 iPad 1 iPad 1 0 89.95 2
## 278 iPad 1 iPad 1 0 92.14 2
## 279 iPad 1 iPad 1 0 101.00 2
## 280 iPad 1 iPad 1 0 104.99 2
## 281 iPad 1 iPad 1 0 115.00 2
## 282 iPad 1 iPad 1 0 124.95 2
## 283 iPad 1 iPad 1 0 125.00 2
## 284 iPad 1 iPad 1 0 129.99 2
## 285 iPad 1 iPad 1 0 165.00 2
## 286 iPad 1 iPad 1 0 175.00 2
## 287 iPad 1 iPad 1 0 250.00 2
## 288 iPad 1 iPad 1 0 279.95 2
## 289 iPad 2 iPad 2 0 0.10 2
## 290 iPad 2 iPad 2 0 15.00 2
## 291 iPad 2 iPad 2 0 19.95 2
## 292 iPad 2 iPad 2 0 59.99 2
## 293 iPad 2 iPad 2 0 65.00 2
## 294 iPad 2 iPad 2 0 69.99 2
## 295 iPad 2 iPad 2 0 74.99 2
## 296 iPad 2 iPad 2 0 89.00 2
## 297 iPad 2 iPad 2 0 95.00 2
## 298 iPad 2 iPad 2 0 119.99 2
## 299 iPad 2 iPad 2 0 128.00 2
## 300 iPad 2 iPad 2 0 135.00 2
## 301 iPad 2 iPad 2 0 144.99 2
## 302 iPad 2 iPad 2 0 145.00 2
## 303 iPad 2 iPad 2 0 149.97 2
## 304 iPad 2 iPad 2 0 150.99 2
## 305 iPad 2 iPad 2 0 162.00 2
## 306 iPad 2 iPad 2 0 169.99 2
## 307 iPad 2 iPad 2 0 170.00 2
## 308 iPad 2 iPad 2 0 172.00 2
## 309 iPad 2 iPad 2 0 179.95 2
## 310 iPad 2 iPad 2 0 204.00 2
## 311 iPad 2 iPad 2 0 220.00 2
## 312 iPad 2 iPad 2 0 350.00 2
## 313 iPad 3 iPad 3 0 0.01 2
## 314 iPad 3 iPad 3 0 25.00 2
## 315 iPad 3 iPad 3 0 49.99 2
## 316 iPad 3 iPad 3 0 89.99 2
## 317 iPad 3 iPad 3 0 99.95 2
## 318 iPad 3 iPad 3 0 125.00 2
## 319 iPad 3 iPad 3 0 140.00 2
## 320 iPad 3 iPad 3 0 179.99 2
## 321 iPad 3 iPad 3 0 180.00 2
## 322 iPad 3 iPad 3 0 209.99 2
## 323 iPad 3 iPad 3 0 215.00 2
## 324 iPad 3 iPad 3 0 229.99 2
## 325 iPad 3 iPad 3 0 239.88 2
## 326 iPad 3 iPad 3 0 239.99 2
## 327 iPad 3 iPad 3 0 299.00 2
## 328 iPad 3 iPad 3 0 314.99 2
## 329 iPad 3 iPad 3 0 450.00 2
## 330 iPad 4 iPad 4 0 80.00 2
## 331 iPad 4 iPad 4 0 99.98 2
## 332 iPad 4 iPad 4 0 107.00 2
## 333 iPad 4 iPad 4 0 125.00 2
## 334 iPad 4 iPad 4 0 195.00 2
## 335 iPad 4 iPad 4 0 199.00 2
## 336 iPad 4 iPad 4 0 209.00 2
## 337 iPad 4 iPad 4 0 240.00 2
## 338 iPad 4 iPad 4 0 255.00 2
## 339 iPad 4 iPad 4 0 265.00 2
## 340 iPad 4 iPad 4 0 269.99 2
## 341 iPad 4 iPad 4 0 285.00 2
## 342 iPad 4 iPad 4 0 295.00 2
## 343 iPad 4 iPad 4 0 299.99 2
## 344 iPad 4 iPad 4 0 305.00 2
## 345 iPad 4 iPad 4 0 309.99 2
## 346 iPad 4 iPad 4 0 310.00 2
## 347 iPad 4 iPad 4 0 315.00 2
## 348 iPad 4 iPad 4 0 324.99 2
## 349 iPad 4 iPad 4 0 325.00 2
## 350 iPad 4 iPad 4 0 344.00 2
## 351 iPad 4 iPad 4 0 350.00 2
## 352 iPad 4 iPad 4 0 367.97 2
## 353 iPad 4 iPad 4 0 375.00 2
## 354 iPad 4 iPad 4 0 500.00 2
## 355 iPad 4 iPad 4 0 588.18 2
## 356 iPad Air iPad Air 0 49.99 2
## 357 iPad Air iPad Air 0 75.00 2
## 358 iPad Air iPad Air 0 89.99 2
## 359 iPad Air iPad Air 0 99.99 2
## 360 iPad Air iPad Air 0 209.00 2
## 361 iPad Air iPad Air 0 245.00 2
## 362 iPad Air iPad Air 0 249.98 2
## 363 iPad Air iPad Air 0 265.00 2
## 364 iPad Air iPad Air 0 280.00 2
## 365 iPad Air iPad Air 0 290.00 2
## 366 iPad Air iPad Air 0 299.00 2
## 367 iPad Air iPad Air 0 319.00 2
## 368 iPad Air iPad Air 0 319.95 2
## 369 iPad Air iPad Air 0 319.99 2
## 370 iPad Air iPad Air 0 320.99 2
## 371 iPad Air iPad Air 0 339.00 2
## 372 iPad Air iPad Air 0 349.00 2
## 373 iPad Air iPad Air 0 349.99 2
## 374 iPad Air iPad Air 0 369.99 2
## 375 iPad Air iPad Air 0 375.00 2
## 376 iPad Air iPad Air 0 379.99 2
## 377 iPad Air iPad Air 0 398.99 2
## 378 iPad Air iPad Air 0 399.99 2
## 379 iPad Air iPad Air 0 449.99 2
## 380 iPad Air iPad Air 0 450.00 2
## 381 iPad Air iPad Air 0 579.99 2
## 382 iPad Air iPad Air 0 648.00 2
## 383 iPad Air iPad Air 0 750.00 2
## 384 iPad Air 2 iPad Air 2 0 99.99 2
## 385 iPad Air 2 iPad Air 2 0 200.00 2
## 386 iPad Air 2 iPad Air 2 0 260.00 2
## 387 iPad Air 2 iPad Air 2 0 300.00 2
## 388 iPad Air 2 iPad Air 2 0 349.99 2
## 389 iPad Air 2 iPad Air 2 0 379.99 2
## 390 iPad Air 2 iPad Air 2 0 419.00 2
## 391 iPad Air 2 iPad Air 2 0 449.00 2
## 392 iPad Air 2 iPad Air 2 0 475.00 2
## 393 iPad Air 2 iPad Air 2 0 549.00 2
## 394 iPad Air 2 iPad Air 2 0 559.99 2
## 395 iPad Air 2 iPad Air 2 0 575.00 2
## 396 iPad Air 2 iPad Air 2 0 639.00 2
## 397 iPad Air 2 iPad Air 2 0 639.99 2
## 398 iPad Air 2 iPad Air 2 0 650.00 2
## 399 iPad Air 2 iPad Air 2 0 729.99 2
## 400 iPad Air 2 iPad Air 2 0 749.00 2
## 401 iPad Air 2 iPad Air 2 0 749.95 2
## 402 iPad Air 2 iPad Air 2 1 465.99 2
## 403 iPad mini Unknown 0 149.99 2
## 404 iPad mini iPad mini 0 5.00 2
## 405 iPad mini iPad mini 0 9.99 2
## 406 iPad mini iPad mini 0 10.00 2
## 407 iPad mini iPad mini 0 30.00 2
## 408 iPad mini iPad mini 0 70.00 2
## 409 iPad mini iPad mini 0 85.00 2
## 410 iPad mini iPad mini 0 90.00 2
## 411 iPad mini iPad mini 0 99.95 2
## 412 iPad mini iPad mini 0 114.99 2
## 413 iPad mini iPad mini 0 115.00 2
## 414 iPad mini iPad mini 0 139.99 2
## 415 iPad mini iPad mini 0 155.00 2
## 416 iPad mini iPad mini 0 165.00 2
## 417 iPad mini iPad mini 0 174.99 2
## 418 iPad mini iPad mini 0 215.00 2
## 419 iPad mini iPad mini 0 219.99 2
## 420 iPad mini iPad mini 0 225.00 2
## 421 iPad mini iPad mini 0 230.00 2
## 422 iPad mini iPad mini 0 239.99 2
## 423 iPad mini iPad mini 0 249.00 2
## 424 iPad mini iPad mini 0 258.98 2
## 425 iPad mini iPad mini 0 280.00 2
## 426 iPad mini iPad mini 0 285.00 2
## 427 iPad mini iPad mini 0 299.99 2
## 428 iPad mini 2 iPad mini 2 0 1.00 2
## 429 iPad mini 2 iPad mini 2 0 99.00 2
## 430 iPad mini 2 iPad mini 2 0 99.99 2
## 431 iPad mini 2 iPad mini 2 0 187.99 2
## 432 iPad mini 2 iPad mini 2 0 230.00 2
## 433 iPad mini 2 iPad mini 2 0 235.00 2
## 434 iPad mini 2 iPad mini 2 0 249.00 2
## 435 iPad mini 2 iPad mini 2 0 269.00 2
## 436 iPad mini 2 iPad mini 2 0 275.00 2
## 437 iPad mini 2 iPad mini 2 0 280.00 2
## 438 iPad mini 2 iPad mini 2 0 289.00 2
## 439 iPad mini 2 iPad mini 2 0 299.00 2
## 440 iPad mini 2 iPad mini 2 0 315.00 2
## 441 iPad mini 2 iPad mini 2 0 325.00 2
## 442 iPad mini 2 iPad mini 2 0 329.00 2
## 443 iPad mini 2 iPad mini 2 0 329.99 2
## 444 iPad mini 2 iPad mini 2 0 349.99 2
## 445 iPad mini 2 iPad mini 2 0 399.99 2
## 446 iPad mini 2 iPad mini 2 0 499.00 2
## 447 iPad mini 3 iPad mini 3 0 0.01 2
## 448 iPad mini 3 iPad mini 3 0 199.00 2
## 449 iPad mini 3 iPad mini 3 0 284.99 2
## 450 iPad mini 3 iPad mini 3 0 299.99 2
## 451 iPad mini 3 iPad mini 3 0 345.00 2
## 452 iPad mini 3 iPad mini 3 0 349.00 2
## 453 iPad mini 3 iPad mini 3 0 375.00 2
## 454 iPad mini 3 iPad mini 3 0 389.99 2
## 455 iPad mini 3 iPad mini 3 0 498.88 2
## 456 iPad mini 3 iPad mini 3 0 500.00 2
## 457 Unknown Unknown 0 0.01 1
## 458 Unknown Unknown 0 1.00 1
## 459 Unknown Unknown 0 2.99 1
## 460 Unknown Unknown 0 3.99 1
## 461 Unknown Unknown 0 4.69 1
## 462 Unknown Unknown 0 4.99 1
## 463 Unknown Unknown 0 5.65 1
## 464 Unknown Unknown 0 7.99 1
## 465 Unknown Unknown 0 8.99 1
## 466 Unknown Unknown 0 9.95 1
## 467 Unknown Unknown 0 10.00 1
## 468 Unknown Unknown 0 14.00 1
## 469 Unknown Unknown 0 14.49 1
## 470 Unknown Unknown 0 14.99 1
## 471 Unknown Unknown 0 17.75 1
## 472 Unknown Unknown 0 24.99 1
## 473 Unknown Unknown 0 28.00 1
## 474 Unknown Unknown 0 28.75 1
## 475 Unknown Unknown 0 30.00 1
## 476 Unknown Unknown 0 30.99 1
## 477 Unknown Unknown 0 32.95 1
## 478 Unknown Unknown 0 35.00 1
## 479 Unknown Unknown 0 37.98 1
## 480 Unknown Unknown 0 41.00 1
## 481 Unknown Unknown 0 43.20 1
## 482 Unknown Unknown 0 44.99 1
## 483 Unknown Unknown 0 45.00 1
## 484 Unknown Unknown 0 49.95 1
## 485 Unknown Unknown 0 49.99 1
## 486 Unknown Unknown 0 51.99 1
## 487 Unknown Unknown 0 52.25 1
## 488 Unknown Unknown 0 57.50 1
## 489 Unknown Unknown 0 58.00 1
## 490 Unknown Unknown 0 59.95 1
## 491 Unknown Unknown 0 59.99 1
## 492 Unknown Unknown 0 63.00 1
## 493 Unknown Unknown 0 65.00 1
## 494 Unknown Unknown 0 69.00 1
## 495 Unknown Unknown 0 70.99 1
## 496 Unknown Unknown 0 76.00 1
## 497 Unknown Unknown 0 87.00 1
## 498 Unknown Unknown 0 89.00 1
## 499 Unknown Unknown 0 90.00 1
## 500 Unknown Unknown 0 91.00 1
## 501 Unknown Unknown 0 99.46 1
## 502 Unknown Unknown 0 109.99 1
## 503 Unknown Unknown 0 110.00 1
## 504 Unknown Unknown 0 114.48 1
## 505 Unknown Unknown 0 116.33 1
## 506 Unknown Unknown 0 118.84 1
## 507 Unknown Unknown 0 118.95 1
## 508 Unknown Unknown 0 119.99 1
## 509 Unknown Unknown 0 124.99 1
## 510 Unknown Unknown 0 125.00 1
## 511 Unknown Unknown 0 127.95 1
## 512 Unknown Unknown 0 137.95 1
## 513 Unknown Unknown 0 139.00 1
## 514 Unknown Unknown 0 139.99 1
## 515 Unknown Unknown 0 145.00 1
## 516 Unknown Unknown 0 149.98 1
## 517 Unknown Unknown 0 150.87 1
## 518 Unknown Unknown 0 154.99 1
## 519 Unknown Unknown 0 155.00 1
## 520 Unknown Unknown 0 167.38 1
## 521 Unknown Unknown 0 168.00 1
## 522 Unknown Unknown 0 170.00 1
## 523 Unknown Unknown 0 174.00 1
## 524 Unknown Unknown 0 177.99 1
## 525 Unknown Unknown 0 179.99 1
## 526 Unknown Unknown 0 180.00 1
## 527 Unknown Unknown 0 182.77 1
## 528 Unknown Unknown 0 186.00 1
## 529 Unknown Unknown 0 188.00 1
## 530 Unknown Unknown 0 190.99 1
## 531 Unknown Unknown 0 193.15 1
## 532 Unknown Unknown 0 195.00 1
## 533 Unknown Unknown 0 196.79 1
## 534 Unknown Unknown 0 199.97 1
## 535 Unknown Unknown 0 210.00 1
## 536 Unknown Unknown 0 214.95 1
## 537 Unknown Unknown 0 215.00 1
## 538 Unknown Unknown 0 220.00 1
## 539 Unknown Unknown 0 224.00 1
## 540 Unknown Unknown 0 225.00 1
## 541 Unknown Unknown 0 229.00 1
## 542 Unknown Unknown 0 229.95 1
## 543 Unknown Unknown 0 229.99 1
## 544 Unknown Unknown 0 230.00 1
## 545 Unknown Unknown 0 232.99 1
## 546 Unknown Unknown 0 239.99 1
## 547 Unknown Unknown 0 255.00 1
## 548 Unknown Unknown 0 259.00 1
## 549 Unknown Unknown 0 259.99 1
## 550 Unknown Unknown 0 260.00 1
## 551 Unknown Unknown 0 265.00 1
## 552 Unknown Unknown 0 275.00 1
## 553 Unknown Unknown 0 276.99 1
## 554 Unknown Unknown 0 279.00 1
## 555 Unknown Unknown 0 285.00 1
## 556 Unknown Unknown 0 289.00 1
## 557 Unknown Unknown 0 295.00 1
## 558 Unknown Unknown 0 309.98 1
## 559 Unknown Unknown 0 325.00 1
## 560 Unknown Unknown 0 329.99 1
## 561 Unknown Unknown 0 334.99 1
## 562 Unknown Unknown 0 339.00 1
## 563 Unknown Unknown 0 340.00 1
## 564 Unknown Unknown 0 347.00 1
## 565 Unknown Unknown 0 349.99 1
## 566 Unknown Unknown 0 380.00 1
## 567 Unknown Unknown 0 389.00 1
## 568 Unknown Unknown 0 395.00 1
## 569 Unknown Unknown 0 408.60 1
## 570 Unknown Unknown 0 415.00 1
## 571 Unknown Unknown 0 419.99 1
## 572 Unknown Unknown 0 425.99 1
## 573 Unknown Unknown 0 426.30 1
## 574 Unknown Unknown 0 439.98 1
## 575 Unknown Unknown 0 470.00 1
## 576 Unknown Unknown 0 473.60 1
## 577 Unknown Unknown 0 480.00 1
## 578 Unknown Unknown 0 520.90 1
## 579 Unknown Unknown 0 535.00 1
## 580 Unknown Unknown 0 550.00 1
## 581 Unknown Unknown 0 554.77 1
## 582 Unknown Unknown 0 561.53 1
## 583 Unknown Unknown 0 590.00 1
## 584 Unknown Unknown 0 595.00 1
## 585 Unknown Unknown 0 599.00 1
## 586 Unknown Unknown 0 639.99 1
## 587 Unknown Unknown 0 640.00 1
## 588 Unknown Unknown 0 679.99 1
## 589 iPad 1 iPad 1 0 9.95 1
## 590 iPad 1 iPad 1 0 19.95 1
## 591 iPad 1 iPad 1 0 29.95 1
## 592 iPad 1 iPad 1 0 33.00 1
## 593 iPad 1 iPad 1 0 35.00 1
## 594 iPad 1 iPad 1 0 39.99 1
## 595 iPad 1 iPad 1 0 42.00 1
## 596 iPad 1 iPad 1 0 48.00 1
## 597 iPad 1 iPad 1 0 48.99 1
## 598 iPad 1 iPad 1 0 49.49 1
## 599 iPad 1 iPad 1 0 52.99 1
## 600 iPad 1 iPad 1 0 54.99 1
## 601 iPad 1 iPad 1 0 55.66 1
## 602 iPad 1 iPad 1 0 56.00 1
## 603 iPad 1 iPad 1 0 59.00 1
## 604 iPad 1 iPad 1 0 59.99 1
## 605 iPad 1 iPad 1 0 64.99 1
## 606 iPad 1 iPad 1 0 72.00 1
## 607 iPad 1 iPad 1 0 74.00 1
## 608 iPad 1 iPad 1 0 74.50 1
## 609 iPad 1 iPad 1 0 74.99 1
## 610 iPad 1 iPad 1 0 78.00 1
## 611 iPad 1 iPad 1 0 79.94 1
## 612 iPad 1 iPad 1 0 82.95 1
## 613 iPad 1 iPad 1 0 82.98 1
## 614 iPad 1 iPad 1 0 85.95 1
## 615 iPad 1 iPad 1 0 89.50 1
## 616 iPad 1 iPad 1 0 91.00 1
## 617 iPad 1 iPad 1 0 92.00 1
## 618 iPad 1 iPad 1 0 93.00 1
## 619 iPad 1 iPad 1 0 94.99 1
## 620 iPad 1 iPad 1 0 96.00 1
## 621 iPad 1 iPad 1 0 98.00 1
## 622 iPad 1 iPad 1 0 99.94 1
## 623 iPad 1 iPad 1 0 102.00 1
## 624 iPad 1 iPad 1 0 104.70 1
## 625 iPad 1 iPad 1 0 109.00 1
## 626 iPad 1 iPad 1 0 109.98 1
## 627 iPad 1 iPad 1 0 112.99 1
## 628 iPad 1 iPad 1 0 114.94 1
## 629 iPad 1 iPad 1 0 119.00 1
## 630 iPad 1 iPad 1 0 120.00 1
## 631 iPad 1 iPad 1 0 120.02 1
## 632 iPad 1 iPad 1 0 124.99 1
## 633 iPad 1 iPad 1 0 129.95 1
## 634 iPad 1 iPad 1 0 130.00 1
## 635 iPad 1 iPad 1 0 145.00 1
## 636 iPad 1 iPad 1 0 149.00 1
## 637 iPad 1 iPad 1 0 149.95 1
## 638 iPad 1 iPad 1 0 149.98 1
## 639 iPad 1 iPad 1 0 149.99 1
## 640 iPad 1 iPad 1 0 155.00 1
## 641 iPad 1 iPad 1 0 159.95 1
## 642 iPad 1 iPad 1 0 169.95 1
## 643 iPad 1 iPad 1 0 170.00 1
## 644 iPad 1 iPad 1 0 174.99 1
## 645 iPad 1 iPad 1 0 190.45 1
## 646 iPad 1 iPad 1 0 198.00 1
## 647 iPad 1 iPad 1 0 199.99 1
## 648 iPad 1 iPad 1 0 200.00 1
## 649 iPad 1 iPad 1 0 209.90 1
## 650 iPad 1 iPad 1 0 220.00 1
## 651 iPad 1 iPad 1 0 225.00 1
## 652 iPad 1 iPad 1 0 227.00 1
## 653 iPad 1 iPad 1 0 229.00 1
## 654 iPad 1 iPad 1 0 229.97 1
## 655 iPad 1 iPad 1 0 229.99 1
## 656 iPad 1 iPad 1 0 235.00 1
## 657 iPad 1 iPad 1 0 245.00 1
## 658 iPad 1 iPad 1 0 269.99 1
## 659 iPad 1 iPad 1 0 275.00 1
## 660 iPad 1 iPad 1 0 289.95 1
## 661 iPad 1 iPad 1 0 499.00 1
## 662 iPad 2 iPad 2 0 5.00 1
## 663 iPad 2 iPad 2 0 20.00 1
## 664 iPad 2 iPad 2 0 25.00 1
## 665 iPad 2 iPad 2 0 29.95 1
## 666 iPad 2 iPad 2 0 29.99 1
## 667 iPad 2 iPad 2 0 39.99 1
## 668 iPad 2 iPad 2 0 45.00 1
## 669 iPad 2 iPad 2 0 49.00 1
## 670 iPad 2 iPad 2 0 60.00 1
## 671 iPad 2 iPad 2 0 66.99 1
## 672 iPad 2 iPad 2 0 69.50 1
## 673 iPad 2 iPad 2 0 69.95 1
## 674 iPad 2 iPad 2 0 71.99 1
## 675 iPad 2 iPad 2 0 72.00 1
## 676 iPad 2 iPad 2 0 74.00 1
## 677 iPad 2 iPad 2 0 79.00 1
## 678 iPad 2 iPad 2 0 79.97 1
## 679 iPad 2 iPad 2 0 89.95 1
## 680 iPad 2 iPad 2 0 92.00 1
## 681 iPad 2 iPad 2 0 92.49 1
## 682 iPad 2 iPad 2 0 96.00 1
## 683 iPad 2 iPad 2 0 97.50 1
## 684 iPad 2 iPad 2 0 104.00 1
## 685 iPad 2 iPad 2 0 106.95 1
## 686 iPad 2 iPad 2 0 109.99 1
## 687 iPad 2 iPad 2 0 110.00 1
## 688 iPad 2 iPad 2 0 111.00 1
## 689 iPad 2 iPad 2 0 111.50 1
## 690 iPad 2 iPad 2 0 112.00 1
## 691 iPad 2 iPad 2 0 115.00 1
## 692 iPad 2 iPad 2 0 119.95 1
## 693 iPad 2 iPad 2 0 121.00 1
## 694 iPad 2 iPad 2 0 124.00 1
## 695 iPad 2 iPad 2 0 127.99 1
## 696 iPad 2 iPad 2 0 134.34 1
## 697 iPad 2 iPad 2 0 134.95 1
## 698 iPad 2 iPad 2 0 139.50 1
## 699 iPad 2 iPad 2 0 139.98 1
## 700 iPad 2 iPad 2 0 141.09 1
## 701 iPad 2 iPad 2 0 142.25 1
## 702 iPad 2 iPad 2 0 142.49 1
## 703 iPad 2 iPad 2 0 145.49 1
## 704 iPad 2 iPad 2 0 146.99 1
## 705 iPad 2 iPad 2 0 147.59 1
## 706 iPad 2 iPad 2 0 147.72 1
## 707 iPad 2 iPad 2 0 152.00 1
## 708 iPad 2 iPad 2 0 153.95 1
## 709 iPad 2 iPad 2 0 153.99 1
## 710 iPad 2 iPad 2 0 157.00 1
## 711 iPad 2 iPad 2 0 158.99 1
## 712 iPad 2 iPad 2 0 159.93 1
## 713 iPad 2 iPad 2 0 159.94 1
## 714 iPad 2 iPad 2 0 159.95 1
## 715 iPad 2 iPad 2 0 164.00 1
## 716 iPad 2 iPad 2 0 169.98 1
## 717 iPad 2 iPad 2 0 171.00 1
## 718 iPad 2 iPad 2 0 173.00 1
## 719 iPad 2 iPad 2 0 182.00 1
## 720 iPad 2 iPad 2 0 184.99 1
## 721 iPad 2 iPad 2 0 189.00 1
## 722 iPad 2 iPad 2 0 189.85 1
## 723 iPad 2 iPad 2 0 189.95 1
## 724 iPad 2 iPad 2 0 190.00 1
## 725 iPad 2 iPad 2 0 190.45 1
## 726 iPad 2 iPad 2 0 193.00 1
## 727 iPad 2 iPad 2 0 194.00 1
## 728 iPad 2 iPad 2 0 194.85 1
## 729 iPad 2 iPad 2 0 194.95 1
## 730 iPad 2 iPad 2 0 195.00 1
## 731 iPad 2 iPad 2 0 198.98 1
## 732 iPad 2 iPad 2 0 199.00 1
## 733 iPad 2 iPad 2 0 204.95 1
## 734 iPad 2 iPad 2 0 209.00 1
## 735 iPad 2 iPad 2 0 210.00 1
## 736 iPad 2 iPad 2 0 211.95 1
## 737 iPad 2 iPad 2 0 212.99 1
## 738 iPad 2 iPad 2 0 215.00 1
## 739 iPad 2 iPad 2 0 215.99 1
## 740 iPad 2 iPad 2 0 219.85 1
## 741 iPad 2 iPad 2 0 225.00 1
## 742 iPad 2 iPad 2 0 227.95 1
## 743 iPad 2 iPad 2 0 228.59 1
## 744 iPad 2 iPad 2 0 229.99 1
## 745 iPad 2 iPad 2 0 234.99 1
## 746 iPad 2 iPad 2 0 235.00 1
## 747 iPad 2 iPad 2 0 239.00 1
## 748 iPad 2 iPad 2 0 239.99 1
## 749 iPad 2 iPad 2 0 249.95 1
## 750 iPad 2 iPad 2 0 255.00 1
## 751 iPad 2 iPad 2 0 269.94 1
## 752 iPad 2 iPad 2 0 279.00 1
## 753 iPad 2 iPad 2 0 279.99 1
## 754 iPad 2 iPad 2 0 280.00 1
## 755 iPad 2 iPad 2 0 288.00 1
## 756 iPad 2 iPad 2 0 289.99 1
## 757 iPad 2 iPad 2 0 294.99 1
## 758 iPad 2 iPad 2 0 298.97 1
## 759 iPad 2 iPad 2 0 310.00 1
## 760 iPad 2 iPad 2 0 315.00 1
## 761 iPad 2 iPad 2 0 330.00 1
## 762 iPad 2 iPad 2 0 340.00 1
## 763 iPad 2 iPad 2 0 349.99 1
## 764 iPad 2 iPad 2 0 393.00 1
## 765 iPad 2 iPad 2 0 395.00 1
## 766 iPad 2 iPad 2 0 396.00 1
## 767 iPad 2 iPad 2 0 406.00 1
## 768 iPad 2 iPad 2 0 417.00 1
## 769 iPad 2 iPad 2 0 425.00 1
## 770 iPad 2 iPad 2 0 525.00 1
## 771 iPad 2 iPad 2 0 700.00 1
## 772 iPad 3 iPad 3 0 0.50 1
## 773 iPad 3 iPad 3 0 5.00 1
## 774 iPad 3 iPad 3 0 7.99 1
## 775 iPad 3 iPad 3 0 45.00 1
## 776 iPad 3 iPad 3 0 50.00 1
## 777 iPad 3 iPad 3 0 69.69 1
## 778 iPad 3 iPad 3 0 71.00 1
## 779 iPad 3 iPad 3 0 75.00 1
## 780 iPad 3 iPad 3 0 80.00 1
## 781 iPad 3 iPad 3 0 85.00 1
## 782 iPad 3 iPad 3 0 95.00 1
## 783 iPad 3 iPad 3 0 105.00 1
## 784 iPad 3 iPad 3 0 115.00 1
## 785 iPad 3 iPad 3 0 120.00 1
## 786 iPad 3 iPad 3 0 129.00 1
## 787 iPad 3 iPad 3 0 134.61 1
## 788 iPad 3 iPad 3 0 159.00 1
## 789 iPad 3 iPad 3 0 159.99 1
## 790 iPad 3 iPad 3 0 165.00 1
## 791 iPad 3 iPad 3 0 179.00 1
## 792 iPad 3 iPad 3 0 179.95 1
## 793 iPad 3 iPad 3 0 184.50 1
## 794 iPad 3 iPad 3 0 184.95 1
## 795 iPad 3 iPad 3 0 188.99 1
## 796 iPad 3 iPad 3 0 189.99 1
## 797 iPad 3 iPad 3 0 190.00 1
## 798 iPad 3 iPad 3 0 196.00 1
## 799 iPad 3 iPad 3 0 199.69 1
## 800 iPad 3 iPad 3 0 200.29 1
## 801 iPad 3 iPad 3 0 204.00 1
## 802 iPad 3 iPad 3 0 210.99 1
## 803 iPad 3 iPad 3 0 214.99 1
## 804 iPad 3 iPad 3 0 219.95 1
## 805 iPad 3 iPad 3 0 228.88 1
## 806 iPad 3 iPad 3 0 229.00 1
## 807 iPad 3 iPad 3 0 229.98 1
## 808 iPad 3 iPad 3 0 234.00 1
## 809 iPad 3 iPad 3 0 235.00 1
## 810 iPad 3 iPad 3 0 235.99 1
## 811 iPad 3 iPad 3 0 246.00 1
## 812 iPad 3 iPad 3 0 248.00 1
## 813 iPad 3 iPad 3 0 249.00 1
## 814 iPad 3 iPad 3 0 249.97 1
## 815 iPad 3 iPad 3 0 252.99 1
## 816 iPad 3 iPad 3 0 259.00 1
## 817 iPad 3 iPad 3 0 269.95 1
## 818 iPad 3 iPad 3 0 289.00 1
## 819 iPad 3 iPad 3 0 289.98 1
## 820 iPad 3 iPad 3 0 290.00 1
## 821 iPad 3 iPad 3 0 295.00 1
## 822 iPad 3 iPad 3 0 298.00 1
## 823 iPad 3 iPad 3 0 299.99 1
## 824 iPad 3 iPad 3 0 319.95 1
## 825 iPad 3 iPad 3 0 339.00 1
## 826 iPad 3 iPad 3 0 350.00 1
## 827 iPad 3 iPad 3 0 380.00 1
## 828 iPad 3 iPad 3 0 395.00 1
## 829 iPad 3 iPad 3 0 463.26 1
## 830 iPad 3 iPad 3 0 494.50 1
## 831 iPad 3 iPad 3 0 600.00 1
## 832 iPad 3 iPad 3 0 649.99 1
## 833 iPad 3 iPad 3 0 699.95 1
## 834 iPad 3 iPad 3 1 9.99 1
## 835 iPad 4 iPad 4 0 1.00 1
## 836 iPad 4 iPad 4 0 7.99 1
## 837 iPad 4 iPad 4 0 8.00 1
## 838 iPad 4 iPad 4 0 10.00 1
## 839 iPad 4 iPad 4 0 20.00 1
## 840 iPad 4 iPad 4 0 35.00 1
## 841 iPad 4 iPad 4 0 38.99 1
## 842 iPad 4 iPad 4 0 39.00 1
## 843 iPad 4 iPad 4 0 65.00 1
## 844 iPad 4 iPad 4 0 79.99 1
## 845 iPad 4 iPad 4 0 99.75 1
## 846 iPad 4 iPad 4 0 99.95 1
## 847 iPad 4 iPad 4 0 115.00 1
## 848 iPad 4 iPad 4 0 119.88 1
## 849 iPad 4 iPad 4 0 119.99 1
## 850 iPad 4 iPad 4 0 139.99 1
## 851 iPad 4 iPad 4 0 144.50 1
## 852 iPad 4 iPad 4 0 149.98 1
## 853 iPad 4 iPad 4 0 155.99 1
## 854 iPad 4 iPad 4 0 160.00 1
## 855 iPad 4 iPad 4 0 174.95 1
## 856 iPad 4 iPad 4 0 185.00 1
## 857 iPad 4 iPad 4 0 189.00 1
## 858 iPad 4 iPad 4 0 215.00 1
## 859 iPad 4 iPad 4 0 218.00 1
## 860 iPad 4 iPad 4 0 219.99 1
## 861 iPad 4 iPad 4 0 220.00 1
## 862 iPad 4 iPad 4 0 224.98 1
## 863 iPad 4 iPad 4 0 224.99 1
## 864 iPad 4 iPad 4 0 229.00 1
## 865 iPad 4 iPad 4 0 237.99 1
## 866 iPad 4 iPad 4 0 238.00 1
## 867 iPad 4 iPad 4 0 239.00 1
## 868 iPad 4 iPad 4 0 239.95 1
## 869 iPad 4 iPad 4 0 244.95 1
## 870 iPad 4 iPad 4 0 244.96 1
## 871 iPad 4 iPad 4 0 245.19 1
## 872 iPad 4 iPad 4 0 249.00 1
## 873 iPad 4 iPad 4 0 249.59 1
## 874 iPad 4 iPad 4 0 249.95 1
## 875 iPad 4 iPad 4 0 254.99 1
## 876 iPad 4 iPad 4 0 259.00 1
## 877 iPad 4 iPad 4 0 260.00 1
## 878 iPad 4 iPad 4 0 261.99 1
## 879 iPad 4 iPad 4 0 263.99 1
## 880 iPad 4 iPad 4 0 264.95 1
## 881 iPad 4 iPad 4 0 264.99 1
## 882 iPad 4 iPad 4 0 270.00 1
## 883 iPad 4 iPad 4 0 276.99 1
## 884 iPad 4 iPad 4 0 279.50 1
## 885 iPad 4 iPad 4 0 280.99 1
## 886 iPad 4 iPad 4 0 284.00 1
## 887 iPad 4 iPad 4 0 289.99 1
## 888 iPad 4 iPad 4 0 291.99 1
## 889 iPad 4 iPad 4 0 299.95 1
## 890 iPad 4 iPad 4 0 303.99 1
## 891 iPad 4 iPad 4 0 304.89 1
## 892 iPad 4 iPad 4 0 319.99 1
## 893 iPad 4 iPad 4 0 324.90 1
## 894 iPad 4 iPad 4 0 329.00 1
## 895 iPad 4 iPad 4 0 339.00 1
## 896 iPad 4 iPad 4 0 340.00 1
## 897 iPad 4 iPad 4 0 345.00 1
## 898 iPad 4 iPad 4 0 349.99 1
## 899 iPad 4 iPad 4 0 399.99 1
## 900 iPad 4 iPad 4 0 410.00 1
## 901 iPad 4 iPad 4 0 419.99 1
## 902 iPad 4 iPad 4 0 425.00 1
## 903 iPad 4 iPad 4 0 445.00 1
## 904 iPad 4 iPad 4 0 479.99 1
## 905 iPad 4 iPad 4 0 520.00 1
## 906 iPad 4 iPad 4 0 540.00 1
## 907 iPad 4 iPad 4 0 544.49 1
## 908 iPad 4 iPad 4 0 559.99 1
## 909 iPad 4 iPad 4 0 573.74 1
## 910 iPad 4 iPad 4 0 649.99 1
## 911 iPad 4 iPad 4 0 650.00 1
## 912 iPad 4 iPad 4 0 695.00 1
## 913 iPad 4 iPad 4 1 279.99 1
## 914 iPad 5 iPad 5 0 300.00 1
## 915 iPad Air Unknown 1 0.99 1
## 916 iPad Air Unknown 1 318.00 1
## 917 iPad Air Unknown 1 550.00 1
## 918 iPad Air iPad Air 0 20.00 1
## 919 iPad Air iPad Air 0 24.99 1
## 920 iPad Air iPad Air 0 25.00 1
## 921 iPad Air iPad Air 0 49.00 1
## 922 iPad Air iPad Air 0 50.00 1
## 923 iPad Air iPad Air 0 80.00 1
## 924 iPad Air iPad Air 0 99.00 1
## 925 iPad Air iPad Air 0 144.95 1
## 926 iPad Air iPad Air 0 149.00 1
## 927 iPad Air iPad Air 0 149.99 1
## 928 iPad Air iPad Air 0 150.00 1
## 929 iPad Air iPad Air 0 160.00 1
## 930 iPad Air iPad Air 0 179.99 1
## 931 iPad Air iPad Air 0 184.99 1
## 932 iPad Air iPad Air 0 185.00 1
## 933 iPad Air iPad Air 0 187.00 1
## 934 iPad Air iPad Air 0 189.99 1
## 935 iPad Air iPad Air 0 199.00 1
## 936 iPad Air iPad Air 0 225.00 1
## 937 iPad Air iPad Air 0 240.00 1
## 938 iPad Air iPad Air 0 242.00 1
## 939 iPad Air iPad Air 0 249.00 1
## 940 iPad Air iPad Air 0 249.99 1
## 941 iPad Air iPad Air 0 255.00 1
## 942 iPad Air iPad Air 0 256.24 1
## 943 iPad Air iPad Air 0 257.83 1
## 944 iPad Air iPad Air 0 259.99 1
## 945 iPad Air iPad Air 0 266.05 1
## 946 iPad Air iPad Air 0 269.85 1
## 947 iPad Air iPad Air 0 270.99 1
## 948 iPad Air iPad Air 0 274.00 1
## 949 iPad Air iPad Air 0 274.99 1
## 950 iPad Air iPad Air 0 275.00 1
## 951 iPad Air iPad Air 0 279.00 1
## 952 iPad Air iPad Air 0 288.00 1
## 953 iPad Air iPad Air 0 289.79 1
## 954 iPad Air iPad Air 0 292.50 1
## 955 iPad Air iPad Air 0 294.99 1
## 956 iPad Air iPad Air 0 299.98 1
## 957 iPad Air iPad Air 0 310.00 1
## 958 iPad Air iPad Air 0 319.85 1
## 959 iPad Air iPad Air 0 322.99 1
## 960 iPad Air iPad Air 0 334.99 1
## 961 iPad Air iPad Air 0 339.99 1
## 962 iPad Air iPad Air 0 344.95 1
## 963 iPad Air iPad Air 0 346.00 1
## 964 iPad Air iPad Air 0 347.24 1
## 965 iPad Air iPad Air 0 349.95 1
## 966 iPad Air iPad Air 0 358.24 1
## 967 iPad Air iPad Air 0 359.99 1
## 968 iPad Air iPad Air 0 360.00 1
## 969 iPad Air iPad Air 0 360.24 1
## 970 iPad Air iPad Air 0 370.00 1
## 971 iPad Air iPad Air 0 374.95 1
## 972 iPad Air iPad Air 0 374.99 1
## 973 iPad Air iPad Air 0 375.99 1
## 974 iPad Air iPad Air 0 380.00 1
## 975 iPad Air iPad Air 0 384.99 1
## 976 iPad Air iPad Air 0 388.99 1
## 977 iPad Air iPad Air 0 389.00 1
## 978 iPad Air iPad Air 0 399.95 1
## 979 iPad Air iPad Air 0 404.99 1
## 980 iPad Air iPad Air 0 408.00 1
## 981 iPad Air iPad Air 0 420.00 1
## 982 iPad Air iPad Air 0 424.95 1
## 983 iPad Air iPad Air 0 429.99 1
## 984 iPad Air iPad Air 0 430.00 1
## 985 iPad Air iPad Air 0 438.00 1
## 986 iPad Air iPad Air 0 439.00 1
## 987 iPad Air iPad Air 0 439.99 1
## 988 iPad Air iPad Air 0 443.09 1
## 989 iPad Air iPad Air 0 455.00 1
## 990 iPad Air iPad Air 0 462.89 1
## 991 iPad Air iPad Air 0 469.99 1
## 992 iPad Air iPad Air 0 495.49 1
## 993 iPad Air iPad Air 0 509.99 1
## 994 iPad Air iPad Air 0 517.89 1
## 995 iPad Air iPad Air 0 539.95 1
## 996 iPad Air iPad Air 0 549.99 1
## 997 iPad Air iPad Air 0 550.00 1
## 998 iPad Air iPad Air 0 558.17 1
## 999 iPad Air iPad Air 0 565.95 1
## 1000 iPad Air iPad Air 0 589.99 1
## 1001 iPad Air iPad Air 0 599.99 1
## 1002 iPad Air iPad Air 0 650.00 1
## 1003 iPad Air iPad Air 0 670.00 1
## 1004 iPad Air iPad Air 0 699.00 1
## 1005 iPad Air iPad Air 0 795.99 1
## 1006 iPad Air iPad Air 0 820.00 1
## 1007 iPad Air iPad Air 1 149.99 1
## 1008 iPad Air iPad Air 1 199.00 1
## 1009 iPad Air iPad Air 1 199.99 1
## 1010 iPad Air iPad Air 1 229.00 1
## 1011 iPad Air iPad Air 1 279.00 1
## 1012 iPad Air iPad Air 1 290.00 1
## 1013 iPad Air iPad Air 1 349.00 1
## 1014 iPad Air iPad Air 1 449.99 1
## 1015 iPad Air 2 iPad Air 2 0 0.01 1
## 1016 iPad Air 2 iPad Air 2 0 1.99 1
## 1017 iPad Air 2 iPad Air 2 0 9.00 1
## 1018 iPad Air 2 iPad Air 2 0 60.00 1
## 1019 iPad Air 2 iPad Air 2 0 99.95 1
## 1020 iPad Air 2 iPad Air 2 0 100.00 1
## 1021 iPad Air 2 iPad Air 2 0 139.00 1
## 1022 iPad Air 2 iPad Air 2 0 229.98 1
## 1023 iPad Air 2 iPad Air 2 0 295.00 1
## 1024 iPad Air 2 iPad Air 2 0 299.00 1
## 1025 iPad Air 2 iPad Air 2 0 299.99 1
## 1026 iPad Air 2 iPad Air 2 0 305.00 1
## 1027 iPad Air 2 iPad Air 2 0 310.00 1
## 1028 iPad Air 2 iPad Air 2 0 319.99 1
## 1029 iPad Air 2 iPad Air 2 0 320.00 1
## 1030 iPad Air 2 iPad Air 2 0 324.99 1
## 1031 iPad Air 2 iPad Air 2 0 339.00 1
## 1032 iPad Air 2 iPad Air 2 0 374.95 1
## 1033 iPad Air 2 iPad Air 2 0 375.00 1
## 1034 iPad Air 2 iPad Air 2 0 380.00 1
## 1035 iPad Air 2 iPad Air 2 0 389.99 1
## 1036 iPad Air 2 iPad Air 2 0 394.99 1
## 1037 iPad Air 2 iPad Air 2 0 395.00 1
## 1038 iPad Air 2 iPad Air 2 0 399.94 1
## 1039 iPad Air 2 iPad Air 2 0 399.95 1
## 1040 iPad Air 2 iPad Air 2 0 410.00 1
## 1041 iPad Air 2 iPad Air 2 0 424.55 1
## 1042 iPad Air 2 iPad Air 2 0 424.65 1
## 1043 iPad Air 2 iPad Air 2 0 424.99 1
## 1044 iPad Air 2 iPad Air 2 0 429.00 1
## 1045 iPad Air 2 iPad Air 2 0 429.95 1
## 1046 iPad Air 2 iPad Air 2 0 429.99 1
## 1047 iPad Air 2 iPad Air 2 0 430.00 1
## 1048 iPad Air 2 iPad Air 2 0 438.99 1
## 1049 iPad Air 2 iPad Air 2 0 439.98 1
## 1050 iPad Air 2 iPad Air 2 0 440.00 1
## 1051 iPad Air 2 iPad Air 2 0 444.99 1
## 1052 iPad Air 2 iPad Air 2 0 445.00 1
## 1053 iPad Air 2 iPad Air 2 0 454.00 1
## 1054 iPad Air 2 iPad Air 2 0 454.68 1
## 1055 iPad Air 2 iPad Air 2 0 459.00 1
## 1056 iPad Air 2 iPad Air 2 0 459.95 1
## 1057 iPad Air 2 iPad Air 2 0 459.99 1
## 1058 iPad Air 2 iPad Air 2 0 469.99 1
## 1059 iPad Air 2 iPad Air 2 0 485.00 1
## 1060 iPad Air 2 iPad Air 2 0 489.99 1
## 1061 iPad Air 2 iPad Air 2 0 490.00 1
## 1062 iPad Air 2 iPad Air 2 0 490.95 1
## 1063 iPad Air 2 iPad Air 2 0 495.99 1
## 1064 iPad Air 2 iPad Air 2 0 499.95 1
## 1065 iPad Air 2 iPad Air 2 0 509.00 1
## 1066 iPad Air 2 iPad Air 2 0 510.00 1
## 1067 iPad Air 2 iPad Air 2 0 514.95 1
## 1068 iPad Air 2 iPad Air 2 0 515.00 1
## 1069 iPad Air 2 iPad Air 2 0 520.00 1
## 1070 iPad Air 2 iPad Air 2 0 528.00 1
## 1071 iPad Air 2 iPad Air 2 0 529.00 1
## 1072 iPad Air 2 iPad Air 2 0 529.95 1
## 1073 iPad Air 2 iPad Air 2 0 529.99 1
## 1074 iPad Air 2 iPad Air 2 0 549.90 1
## 1075 iPad Air 2 iPad Air 2 0 549.95 1
## 1076 iPad Air 2 iPad Air 2 0 559.00 1
## 1077 iPad Air 2 iPad Air 2 0 579.99 1
## 1078 iPad Air 2 iPad Air 2 0 585.99 1
## 1079 iPad Air 2 iPad Air 2 0 589.00 1
## 1080 iPad Air 2 iPad Air 2 0 590.00 1
## 1081 iPad Air 2 iPad Air 2 0 595.00 1
## 1082 iPad Air 2 iPad Air 2 0 598.98 1
## 1083 iPad Air 2 iPad Air 2 0 600.00 1
## 1084 iPad Air 2 iPad Air 2 0 614.99 1
## 1085 iPad Air 2 iPad Air 2 0 615.99 1
## 1086 iPad Air 2 iPad Air 2 0 619.00 1
## 1087 iPad Air 2 iPad Air 2 0 624.99 1
## 1088 iPad Air 2 iPad Air 2 0 625.00 1
## 1089 iPad Air 2 iPad Air 2 0 629.00 1
## 1090 iPad Air 2 iPad Air 2 0 630.00 1
## 1091 iPad Air 2 iPad Air 2 0 634.99 1
## 1092 iPad Air 2 iPad Air 2 0 645.00 1
## 1093 iPad Air 2 iPad Air 2 0 645.99 1
## 1094 iPad Air 2 iPad Air 2 0 649.95 1
## 1095 iPad Air 2 iPad Air 2 0 649.99 1
## 1096 iPad Air 2 iPad Air 2 0 659.49 1
## 1097 iPad Air 2 iPad Air 2 0 660.00 1
## 1098 iPad Air 2 iPad Air 2 0 675.00 1
## 1099 iPad Air 2 iPad Air 2 0 679.95 1
## 1100 iPad Air 2 iPad Air 2 0 679.99 1
## 1101 iPad Air 2 iPad Air 2 0 680.00 1
## 1102 iPad Air 2 iPad Air 2 0 710.00 1
## 1103 iPad Air 2 iPad Air 2 0 730.00 1
## 1104 iPad Air 2 iPad Air 2 0 740.00 1
## 1105 iPad Air 2 iPad Air 2 0 749.99 1
## 1106 iPad Air 2 iPad Air 2 0 785.00 1
## 1107 iPad Air 2 iPad Air 2 0 789.00 1
## 1108 iPad Air 2 iPad Air 2 0 789.99 1
## 1109 iPad Air 2 iPad Air 2 0 795.00 1
## 1110 iPad Air 2 iPad Air 2 0 798.00 1
## 1111 iPad Air 2 iPad Air 2 0 799.00 1
## 1112 iPad Air 2 iPad Air 2 0 800.00 1
## 1113 iPad Air 2 iPad Air 2 0 829.99 1
## 1114 iPad Air 2 iPad Air 2 0 879.99 1
## 1115 iPad Air 2 iPad Air 2 0 899.99 1
## 1116 iPad Air 2 iPad Air 2 0 900.00 1
## 1117 iPad Air 2 iPad Air 2 0 939.00 1
## 1118 iPad Air 2 iPad Air 2 1 10.00 1
## 1119 iPad Air 2 iPad Air 2 1 59.00 1
## 1120 iPad Air 2 iPad Air 2 1 619.99 1
## 1121 iPad Air 2 iPad Air 2 1 800.00 1
## 1122 iPad Air 2 iPad Air 2 2 499.00 1
## 1123 iPad mini Unknown 0 190.00 1
## 1124 iPad mini Unknown 0 409.99 1
## 1125 iPad mini Unknown 0 999.99 1
## 1126 iPad mini iPad mini 0 0.98 1
## 1127 iPad mini iPad mini 0 10.99 1
## 1128 iPad mini iPad mini 0 19.50 1
## 1129 iPad mini iPad mini 0 19.99 1
## 1130 iPad mini iPad mini 0 29.99 1
## 1131 iPad mini iPad mini 0 40.00 1
## 1132 iPad mini iPad mini 0 42.00 1
## 1133 iPad mini iPad mini 0 49.95 1
## 1134 iPad mini iPad mini 0 49.99 1
## 1135 iPad mini iPad mini 0 59.99 1
## 1136 iPad mini iPad mini 0 62.00 1
## 1137 iPad mini iPad mini 0 74.95 1
## 1138 iPad mini iPad mini 0 74.99 1
## 1139 iPad mini iPad mini 0 79.00 1
## 1140 iPad mini iPad mini 0 79.99 1
## 1141 iPad mini iPad mini 0 84.99 1
## 1142 iPad mini iPad mini 0 89.00 1
## 1143 iPad mini iPad mini 0 109.00 1
## 1144 iPad mini iPad mini 0 109.99 1
## 1145 iPad mini iPad mini 0 110.00 1
## 1146 iPad mini iPad mini 0 112.00 1
## 1147 iPad mini iPad mini 0 113.00 1
## 1148 iPad mini iPad mini 0 118.00 1
## 1149 iPad mini iPad mini 0 119.98 1
## 1150 iPad mini iPad mini 0 129.00 1
## 1151 iPad mini iPad mini 0 129.95 1
## 1152 iPad mini iPad mini 0 129.99 1
## 1153 iPad mini iPad mini 0 135.00 1
## 1154 iPad mini iPad mini 0 139.00 1
## 1155 iPad mini iPad mini 0 140.00 1
## 1156 iPad mini iPad mini 0 144.99 1
## 1157 iPad mini iPad mini 0 145.00 1
## 1158 iPad mini iPad mini 0 149.59 1
## 1159 iPad mini iPad mini 0 149.95 1
## 1160 iPad mini iPad mini 0 149.99 1
## 1161 iPad mini iPad mini 0 159.95 1
## 1162 iPad mini iPad mini 0 160.57 1
## 1163 iPad mini iPad mini 0 168.00 1
## 1164 iPad mini iPad mini 0 169.99 1
## 1165 iPad mini iPad mini 0 170.00 1
## 1166 iPad mini iPad mini 0 171.95 1
## 1167 iPad mini iPad mini 0 176.27 1
## 1168 iPad mini iPad mini 0 178.99 1
## 1169 iPad mini iPad mini 0 179.96 1
## 1170 iPad mini iPad mini 0 180.00 1
## 1171 iPad mini iPad mini 0 181.00 1
## 1172 iPad mini iPad mini 0 184.99 1
## 1173 iPad mini iPad mini 0 185.00 1
## 1174 iPad mini iPad mini 0 185.49 1
## 1175 iPad mini iPad mini 0 187.89 1
## 1176 iPad mini iPad mini 0 188.88 1
## 1177 iPad mini iPad mini 0 190.00 1
## 1178 iPad mini iPad mini 0 194.29 1
## 1179 iPad mini iPad mini 0 195.00 1
## 1180 iPad mini iPad mini 0 198.00 1
## 1181 iPad mini iPad mini 0 199.97 1
## 1182 iPad mini iPad mini 0 205.00 1
## 1183 iPad mini iPad mini 0 208.00 1
## 1184 iPad mini iPad mini 0 208.99 1
## 1185 iPad mini iPad mini 0 209.00 1
## 1186 iPad mini iPad mini 0 209.85 1
## 1187 iPad mini iPad mini 0 209.99 1
## 1188 iPad mini iPad mini 0 211.50 1
## 1189 iPad mini iPad mini 0 212.99 1
## 1190 iPad mini iPad mini 0 214.98 1
## 1191 iPad mini iPad mini 0 215.99 1
## 1192 iPad mini iPad mini 0 219.00 1
## 1193 iPad mini iPad mini 0 220.00 1
## 1194 iPad mini iPad mini 0 227.88 1
## 1195 iPad mini iPad mini 0 235.00 1
## 1196 iPad mini iPad mini 0 239.00 1
## 1197 iPad mini iPad mini 0 240.00 1
## 1198 iPad mini iPad mini 0 241.88 1
## 1199 iPad mini iPad mini 0 244.97 1
## 1200 iPad mini iPad mini 0 249.95 1
## 1201 iPad mini iPad mini 0 252.88 1
## 1202 iPad mini iPad mini 0 255.00 1
## 1203 iPad mini iPad mini 0 258.88 1
## 1204 iPad mini iPad mini 0 259.00 1
## 1205 iPad mini iPad mini 0 260.00 1
## 1206 iPad mini iPad mini 0 265.00 1
## 1207 iPad mini iPad mini 0 265.99 1
## 1208 iPad mini iPad mini 0 271.00 1
## 1209 iPad mini iPad mini 0 279.00 1
## 1210 iPad mini iPad mini 0 279.50 1
## 1211 iPad mini iPad mini 0 279.99 1
## 1212 iPad mini iPad mini 0 289.00 1
## 1213 iPad mini iPad mini 0 289.99 1
## 1214 iPad mini iPad mini 0 295.00 1
## 1215 iPad mini iPad mini 0 298.00 1
## 1216 iPad mini iPad mini 0 299.95 1
## 1217 iPad mini iPad mini 0 310.00 1
## 1218 iPad mini iPad mini 0 315.00 1
## 1219 iPad mini iPad mini 0 320.00 1
## 1220 iPad mini iPad mini 0 334.95 1
## 1221 iPad mini iPad mini 0 339.99 1
## 1222 iPad mini iPad mini 0 348.60 1
## 1223 iPad mini iPad mini 0 349.99 1
## 1224 iPad mini iPad mini 0 351.00 1
## 1225 iPad mini iPad mini 0 358.87 1
## 1226 iPad mini iPad mini 0 370.00 1
## 1227 iPad mini iPad mini 0 375.00 1
## 1228 iPad mini iPad mini 0 379.99 1
## 1229 iPad mini iPad mini 0 385.00 1
## 1230 iPad mini iPad mini 0 387.45 1
## 1231 iPad mini iPad mini 0 388.30 1
## 1232 iPad mini iPad mini 0 397.75 1
## 1233 iPad mini iPad mini 0 398.99 1
## 1234 iPad mini iPad mini 0 399.99 1
## 1235 iPad mini iPad mini 0 429.00 1
## 1236 iPad mini iPad mini 0 429.99 1
## 1237 iPad mini iPad mini 0 475.00 1
## 1238 iPad mini iPad mini 0 499.99 1
## 1239 iPad mini iPad mini 0 720.12 1
## 1240 iPad mini iPad mini 0 999.00 1
## 1241 iPad mini 2 iPad mini 2 0 0.01 1
## 1242 iPad mini 2 iPad mini 2 0 10.00 1
## 1243 iPad mini 2 iPad mini 2 0 25.00 1
## 1244 iPad mini 2 iPad mini 2 0 49.99 1
## 1245 iPad mini 2 iPad mini 2 0 79.95 1
## 1246 iPad mini 2 iPad mini 2 0 99.97 1
## 1247 iPad mini 2 iPad mini 2 0 119.00 1
## 1248 iPad mini 2 iPad mini 2 0 129.99 1
## 1249 iPad mini 2 iPad mini 2 0 130.00 1
## 1250 iPad mini 2 iPad mini 2 0 145.00 1
## 1251 iPad mini 2 iPad mini 2 0 149.00 1
## 1252 iPad mini 2 iPad mini 2 0 149.95 1
## 1253 iPad mini 2 iPad mini 2 0 150.00 1
## 1254 iPad mini 2 iPad mini 2 0 155.00 1
## 1255 iPad mini 2 iPad mini 2 0 160.00 1
## 1256 iPad mini 2 iPad mini 2 0 185.00 1
## 1257 iPad mini 2 iPad mini 2 0 195.00 1
## 1258 iPad mini 2 iPad mini 2 0 199.00 1
## 1259 iPad mini 2 iPad mini 2 0 201.99 1
## 1260 iPad mini 2 iPad mini 2 0 209.98 1
## 1261 iPad mini 2 iPad mini 2 0 210.00 1
## 1262 iPad mini 2 iPad mini 2 0 215.00 1
## 1263 iPad mini 2 iPad mini 2 0 217.00 1
## 1264 iPad mini 2 iPad mini 2 0 222.72 1
## 1265 iPad mini 2 iPad mini 2 0 223.00 1
## 1266 iPad mini 2 iPad mini 2 0 229.00 1
## 1267 iPad mini 2 iPad mini 2 0 237.00 1
## 1268 iPad mini 2 iPad mini 2 0 238.80 1
## 1269 iPad mini 2 iPad mini 2 0 239.00 1
## 1270 iPad mini 2 iPad mini 2 0 239.99 1
## 1271 iPad mini 2 iPad mini 2 0 245.00 1
## 1272 iPad mini 2 iPad mini 2 0 248.18 1
## 1273 iPad mini 2 iPad mini 2 0 259.95 1
## 1274 iPad mini 2 iPad mini 2 0 260.00 1
## 1275 iPad mini 2 iPad mini 2 0 264.99 1
## 1276 iPad mini 2 iPad mini 2 0 279.99 1
## 1277 iPad mini 2 iPad mini 2 0 289.95 1
## 1278 iPad mini 2 iPad mini 2 0 295.00 1
## 1279 iPad mini 2 iPad mini 2 0 299.99 1
## 1280 iPad mini 2 iPad mini 2 0 308.00 1
## 1281 iPad mini 2 iPad mini 2 0 310.00 1
## 1282 iPad mini 2 iPad mini 2 0 319.98 1
## 1283 iPad mini 2 iPad mini 2 0 319.99 1
## 1284 iPad mini 2 iPad mini 2 0 327.58 1
## 1285 iPad mini 2 iPad mini 2 0 339.00 1
## 1286 iPad mini 2 iPad mini 2 0 339.99 1
## 1287 iPad mini 2 iPad mini 2 0 350.25 1
## 1288 iPad mini 2 iPad mini 2 0 376.00 1
## 1289 iPad mini 2 iPad mini 2 0 379.99 1
## 1290 iPad mini 2 iPad mini 2 0 380.00 1
## 1291 iPad mini 2 iPad mini 2 0 385.00 1
## 1292 iPad mini 2 iPad mini 2 0 387.00 1
## 1293 iPad mini 2 iPad mini 2 0 395.00 1
## 1294 iPad mini 2 iPad mini 2 0 400.00 1
## 1295 iPad mini 2 iPad mini 2 0 429.99 1
## 1296 iPad mini 2 iPad mini 2 0 430.00 1
## 1297 iPad mini 2 iPad mini 2 0 449.00 1
## 1298 iPad mini 2 iPad mini 2 0 450.00 1
## 1299 iPad mini 2 iPad mini 2 0 458.00 1
## 1300 iPad mini 2 iPad mini 2 0 460.00 1
## 1301 iPad mini 2 iPad mini 2 0 469.00 1
## 1302 iPad mini 2 iPad mini 2 0 500.00 1
## 1303 iPad mini 2 iPad mini 2 0 509.00 1
## 1304 iPad mini 2 iPad mini 2 0 550.00 1
## 1305 iPad mini 2 iPad mini 2 0 575.00 1
## 1306 iPad mini 2 iPad mini 2 0 595.00 1
## 1307 iPad mini 3 iPad mini 3 0 0.45 1
## 1308 iPad mini 3 iPad mini 3 0 9.95 1
## 1309 iPad mini 3 iPad mini 3 0 25.00 1
## 1310 iPad mini 3 iPad mini 3 0 100.00 1
## 1311 iPad mini 3 iPad mini 3 0 149.00 1
## 1312 iPad mini 3 iPad mini 3 0 175.00 1
## 1313 iPad mini 3 iPad mini 3 0 197.97 1
## 1314 iPad mini 3 iPad mini 3 0 199.99 1
## 1315 iPad mini 3 iPad mini 3 0 249.00 1
## 1316 iPad mini 3 iPad mini 3 0 250.00 1
## 1317 iPad mini 3 iPad mini 3 0 290.00 1
## 1318 iPad mini 3 iPad mini 3 0 295.95 1
## 1319 iPad mini 3 iPad mini 3 0 299.00 1
## 1320 iPad mini 3 iPad mini 3 0 309.95 1
## 1321 iPad mini 3 iPad mini 3 0 329.00 1
## 1322 iPad mini 3 iPad mini 3 0 331.99 1
## 1323 iPad mini 3 iPad mini 3 0 332.50 1
## 1324 iPad mini 3 iPad mini 3 0 334.00 1
## 1325 iPad mini 3 iPad mini 3 0 335.00 1
## 1326 iPad mini 3 iPad mini 3 0 339.50 1
## 1327 iPad mini 3 iPad mini 3 0 339.98 1
## 1328 iPad mini 3 iPad mini 3 0 340.00 1
## 1329 iPad mini 3 iPad mini 3 0 349.95 1
## 1330 iPad mini 3 iPad mini 3 0 349.99 1
## 1331 iPad mini 3 iPad mini 3 0 359.00 1
## 1332 iPad mini 3 iPad mini 3 0 359.99 1
## 1333 iPad mini 3 iPad mini 3 0 370.00 1
## 1334 iPad mini 3 iPad mini 3 0 379.95 1
## 1335 iPad mini 3 iPad mini 3 0 379.99 1
## 1336 iPad mini 3 iPad mini 3 0 380.00 1
## 1337 iPad mini 3 iPad mini 3 0 385.00 1
## 1338 iPad mini 3 iPad mini 3 0 394.99 1
## 1339 iPad mini 3 iPad mini 3 0 399.00 1
## 1340 iPad mini 3 iPad mini 3 0 419.95 1
## 1341 iPad mini 3 iPad mini 3 0 419.99 1
## 1342 iPad mini 3 iPad mini 3 0 425.00 1
## 1343 iPad mini 3 iPad mini 3 0 426.99 1
## 1344 iPad mini 3 iPad mini 3 0 439.99 1
## 1345 iPad mini 3 iPad mini 3 0 445.95 1
## 1346 iPad mini 3 iPad mini 3 0 449.95 1
## 1347 iPad mini 3 iPad mini 3 0 450.00 1
## 1348 iPad mini 3 iPad mini 3 0 459.99 1
## 1349 iPad mini 3 iPad mini 3 0 460.00 1
## 1350 iPad mini 3 iPad mini 3 0 469.99 1
## 1351 iPad mini 3 iPad mini 3 0 475.00 1
## 1352 iPad mini 3 iPad mini 3 0 485.00 1
## 1353 iPad mini 3 iPad mini 3 0 510.00 1
## 1354 iPad mini 3 iPad mini 3 0 525.00 1
## 1355 iPad mini 3 iPad mini 3 0 529.99 1
## 1356 iPad mini 3 iPad mini 3 0 549.99 1
## 1357 iPad mini 3 iPad mini 3 0 550.00 1
## 1358 iPad mini 3 iPad mini 3 0 559.99 1
## 1359 iPad mini 3 iPad mini 3 0 569.00 1
## 1360 iPad mini 3 iPad mini 3 0 575.00 1
## 1361 iPad mini 3 iPad mini 3 0 579.99 1
## 1362 iPad mini 3 iPad mini 3 0 609.99 1
## 1363 iPad mini 3 iPad mini 3 0 614.99 1
## 1364 iPad mini 3 iPad mini 3 0 639.99 1
## 1365 iPad mini 3 iPad mini 3 0 650.00 1
## 1366 iPad mini 3 iPad mini 3 0 689.99 1
## 1367 iPad mini 3 iPad mini 3 0 799.99 1
## 1368 iPad mini 3 iPad mini 3 0 948.98 1
## 1369 iPad mini Retina iPad mini Retina 0 160.00 1
## 1370 iPad mini Retina iPad mini Retina 0 235.00 1
## 1371 iPad mini Retina iPad mini Retina 0 250.00 1
## 1372 iPad mini Retina iPad mini Retina 0 299.00 1
## 1373 iPad mini Retina iPad mini Retina 0 303.67 1
## 1374 iPad mini Retina iPad mini Retina 0 339.00 1
## 1375 iPad mini Retina iPad mini Retina 0 350.00 1
## 1376 iPad mini Retina iPad mini Retina 0 420.00 1glb_allobs_df[glb_allobs_df$UniqueID == 12156, "prdline.my"] <- "iPad 1"
glb_allobs_df[glb_allobs_df$UniqueID == 11811, "prdline.my"] <- "iPad 2"
glb_allobs_df[glb_allobs_df$UniqueID == 11767, "prdline.my"] <- "iPad 2"
glb_allobs_df[glb_allobs_df$UniqueID == 11767, "storage"] <- "32"
# dsp_obs(list(prdline.my="Unknown"), all=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
tmp_allobs_df <- glb_allobs_df[, "prdline.my", FALSE]
names(tmp_allobs_df) <- "old.prdline.my"
glb_allobs_df$prdline.my <-
plyr::revalue(glb_allobs_df$prdline.my, c(
# "iPad 1" = "iPad",
# "iPad 2" = "iPad2+",
"iPad 3" = "iPad 3+",
"iPad 4" = "iPad 3+",
"iPad 5" = "iPad 3+",
"iPad Air" = "iPadAir",
"iPad Air 2" = "iPadAir",
"iPad mini" = "iPadmini",
"iPad mini 2" = "iPadmini 2+",
"iPad mini 3" = "iPadmini 2+",
"iPad mini Retina" = "iPadmini 2+"
))
tmp_allobs_df$prdline.my <- glb_allobs_df[, "prdline.my"]
print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my", "old.prdline.my")))## prdline.my old.prdline.my .n
## 1 iPad 2 iPad 2 442
## 2 iPadmini iPad mini 393
## 3 iPad 1 iPad 1 314
## 4 Unknown Unknown 285
## 5 iPadAir iPad Air 257
## 6 iPadAir iPad Air 2 233
## 7 iPad 3+ iPad 4 225
## 8 iPad 3+ iPad 3 208
## 9 iPadmini 2+ iPad mini 2 163
## 10 iPadmini 2+ iPad mini 3 128
## 11 iPadmini 2+ iPad mini Retina 8
## 12 iPad 3+ iPad 5 1print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my")))## prdline.my .n
## 1 iPadAir 490
## 2 iPad 2 442
## 3 iPad 3+ 434
## 4 iPadmini 393
## 5 iPad 1 314
## 6 iPadmini 2+ 299
## 7 Unknown 285glb_allobs_df$prdline.my.fctr <- as.factor(glb_allobs_df$prdline.my)
glb_allobs_df$storage.fctr <- as.factor(glb_allobs_df$storage)
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
glb_full_DTM_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'corpus_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_vctr' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 extract.features_bind.DXM 8 0 38.222 87.338 49.117
## 10 extract.features_end 9 0 87.339 NA NAmyplt_chunk(extract.features_chunk_df)## label step_major
## 9 extract.features_bind.DXM 8
## 5 extract.features_build.corpus 4
## 7 extract.features_report.DTM 6
## 3 extract.features_process.text 3
## 6 extract.features_extract.DTM 5
## 2 extract.features_factorize.str.vars 2
## 8 extract.features_bind.DTM 7
## 1 extract.features_bgn 1
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed duration
## 9 0 38.222 87.338 49.117 49.116
## 5 0 23.187 34.391 11.204 11.204
## 7 0 35.665 37.797 2.132 2.132
## 3 0 21.344 23.182 1.838 1.838
## 6 0 34.392 35.664 1.272 1.272
## 2 0 20.109 21.344 1.235 1.235
## 8 0 37.797 38.222 0.425 0.425
## 1 0 20.096 20.109 0.013 0.013
## 4 1 23.182 23.186 0.004 0.004
## [1] "Total Elapsed Time: 87.338 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 extract.features 3 0 20.089 88.682 68.593
## 6 cluster.data 4 0 88.683 NA NAStep 4.0: cluster data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 6 cluster.data 4 0 88.683 89.748 1.065
## 7 manage.missing.data 4 1 89.749 NA NA# If mice crashes with error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'State' of mode 'function' was not found
# consider excluding 'State' as a feature
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1444 999 31
## cellular.fctr D.terms.n.post.stop D.terms.n.post.stop.log
## 1597 1521 1521
## D.TfIdf.sum.post.stop D.terms.n.post.stem D.terms.n.post.stem.log
## 1521 1521 1521
## D.TfIdf.sum.post.stem D.T.condit D.T.use
## 1521 2161 2366
## D.T.scratch D.T.new D.T.good
## 2371 2501 2460
## D.T.ipad D.T.screen D.T.great
## 2425 2444 2532
## D.T.work D.T.excel D.nwrds.log
## 2459 2557 1520
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 1521 1521 1521
## D.nchrs.log D.nuppr.log D.ndgts.log
## 1520 1522 2426
## D.npnct01.log D.npnct02.log D.npnct03.log
## 2579 2657 2614
## D.npnct04.log D.npnct05.log D.npnct06.log
## 2657 2592 2554
## D.npnct07.log D.npnct08.log D.npnct09.log
## 2656 2581 2641
## D.npnct10.log D.npnct11.log D.npnct12.log
## 2648 2301 2537
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1932 2582 2637
## D.npnct16.log D.npnct17.log D.npnct18.log
## 2546 2657 2656
## D.npnct19.log D.npnct20.log D.npnct21.log
## 2657 2657 2657
## D.npnct22.log D.npnct23.log D.npnct24.log
## 2657 2657 1520
## D.npnct25.log D.npnct26.log D.npnct27.log
## 2657 2657 2657
## D.npnct28.log D.npnct29.log D.npnct30.log
## 2649 2657 2657
## D.nstopwrds.log D.P.http D.P.mini
## 1663 2657 2623
## D.P.air
## 2637
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520# glb_allobs_df <- na.omit(glb_allobs_df)
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col],
inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
# complete(mice()) changes attributes of factors even though values don't change
for (col in ret_vars) {
if (inherits(out_impent_df[, col], "factor")) {
if (identical(as.numeric(out_impent_df[, col]),
as.numeric(inp_impent_df[, col])))
ret_vars <- setdiff(ret_vars, col)
}
}
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
}
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1444 999 31
## cellular.fctr D.terms.n.post.stop D.terms.n.post.stop.log
## 1597 1521 1521
## D.TfIdf.sum.post.stop D.terms.n.post.stem D.terms.n.post.stem.log
## 1521 1521 1521
## D.TfIdf.sum.post.stem D.T.condit D.T.use
## 1521 2161 2366
## D.T.scratch D.T.new D.T.good
## 2371 2501 2460
## D.T.ipad D.T.screen D.T.great
## 2425 2444 2532
## D.T.work D.T.excel D.nwrds.log
## 2459 2557 1520
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 1521 1521 1521
## D.nchrs.log D.nuppr.log D.ndgts.log
## 1520 1522 2426
## D.npnct01.log D.npnct02.log D.npnct03.log
## 2579 2657 2614
## D.npnct04.log D.npnct05.log D.npnct06.log
## 2657 2592 2554
## D.npnct07.log D.npnct08.log D.npnct09.log
## 2656 2581 2641
## D.npnct10.log D.npnct11.log D.npnct12.log
## 2648 2301 2537
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1932 2582 2637
## D.npnct16.log D.npnct17.log D.npnct18.log
## 2546 2657 2656
## D.npnct19.log D.npnct20.log D.npnct21.log
## 2657 2657 2657
## D.npnct22.log D.npnct23.log D.npnct24.log
## 2657 2657 1520
## D.npnct25.log D.npnct26.log D.npnct27.log
## 2657 2657 2657
## D.npnct28.log D.npnct29.log D.npnct30.log
## 2649 2657 2657
## D.nstopwrds.log D.P.http D.P.mini
## 1663 2657 2623
## D.P.air
## 2637
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520Step 4.1: manage missing data
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
require(entropy)
require(tidyr)
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print("Clustering features: ")
print(cluster_vars <- grep(paste0("[",
toupper(paste0(substr(glb_txt_vars, 1, 1), collapse="")),
"]\\.[PT]\\."),
names(glb_allobs_df), value=TRUE))
print(sprintf("glb_allobs_df Entropy: %0.4f",
allobs_ent <- entropy(table(glb_allobs_df[, glb_rsp_var]), method="ML")))
category_df <- as.data.frame(table(glb_allobs_df[, glb_category_var],
glb_allobs_df[, glb_rsp_var]))
names(category_df)[c(1, 2)] <- c(glb_category_var, glb_rsp_var)
category_df <- do.call(tidyr::spread, list(category_df, glb_rsp_var, "Freq"))
tmp.entropy <- sapply(1:nrow(category_df),
function(row) entropy(as.numeric(category_df[row, -1]), method="ML"))
tmp.knt <- sapply(1:nrow(category_df),
function(row) sum(as.numeric(category_df[row, -1])))
category_df$.entropy <- tmp.entropy; category_df$.knt <- tmp.knt
print(sprintf("glb_allobs_df$%s Entropy: %0.4f (%0.4f pct)", glb_category_var,
category_ent <- weighted.mean(category_df$.entropy, category_df$.knt),
100 * category_ent / allobs_ent))
print(category_df)
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
for (grp in sort(unique(glb_allobs_df[, glb_category_var]))) {
print(sprintf("Category: %s", grp))
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df[, glb_category_var] == grp, ]
dstns_dist <- dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_mtrx <- as.matrix(dstns_dist)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_rsp_var, glb_category_var, glb_txt_vars, cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_rsp_var, glb_category_var, glb_txt_vars, cluster_vars)])
set.seed(glb_cluster.seed)
clusters <- hclust(dstns_dist, method = "ward.D2")
#plot(clusters, labels=NULL, hang=-1)
myplclust(clusters, lab.col=unclass(ctgry_allobs_df[, glb_rsp_var]))
#clusterGroups = cutree(clusters, k=7)
clusterGroups <- cutreeDynamic(clusters, minClusterSize=20, method="tree", deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#print(ctgry_allobs_df[which(clusterGroups == 1), c("UniqueID", "Popular", "Headline")])
#print(ctgry_allobs_df[(clusterGroups == 1) & !is.na(ctgry_allobs_df$Popular) & (ctgry_allobs_df$Popular==1), c("UniqueID", "Popular", "Headline")])
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#summary(factor(clusterGroups))
# clusterGroups <- clusterGroups +
# 100 * # has to be > max(table(glb_allobs_df[, glb_category_var].fctr) / minClusterSize=20)
# which(levels(glb_allobs_df[, glb_category_var].fctr) == grp)
# table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
# add to glb_allobs_df - then split the data again
glb_allobs_df[glb_allobs_df[, glb_category_var]==grp,]$.clusterid <- clusterGroups
#print(unique(glb_allobs_df$.clusterid))
#print(glb_feats_df[glb_feats_df$id == ".clusterid.fctr", ])
}
cluster_df <- as.data.frame(table(glb_allobs_df[, glb_category_var],
glb_allobs_df[, ".clusterid"],
glb_allobs_df[, glb_rsp_var]))
cluster_df <- subset(cluster_df, Freq > 0)
names(cluster_df)[c(1, 2, 3)] <- c(glb_category_var, ".clusterid", glb_rsp_var)
# spread(unite(cluster_df, prdline.my.clusterid, prdline.my, .clusterid),
# sold.fctr, Freq)
cluster_df <- do.call(tidyr::unite,
list(cluster_df, paste0(glb_category_var, ".clusterid"),
grep(glb_category_var, names(cluster_df)),
grep(".clusterid", names(cluster_df))))
cluster_df <- do.call(tidyr::spread,
list(cluster_df, glb_rsp_var, "Freq"))
tmp.entropy <- sapply(1:nrow(cluster_df),
function(row) entropy(as.numeric(cluster_df[row, -1]), method="ML"))
tmp.knt <- sapply(1:nrow(cluster_df),
function(row) sum(as.numeric(cluster_df[row, -1])))
cluster_df$.entropy <- tmp.entropy; cluster_df$.knt <- tmp.knt
print(sprintf("glb_allobs_df$%s$.clusterid Entropy: %0.4f (%0.4f pct)",
glb_category_var,
cluster_ent <- weighted.mean(cluster_df$.entropy, cluster_df$.knt),
100 * cluster_ent / category_ent))
print(cluster_df)
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
".clusterid")
glb_interaction_only_features[paste0(glb_category_var, ".fctr")] <-
c(".clusterid.fctr")
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
cluster_vars)
}## Loading required package: proxy
##
## Attaching package: 'proxy'
##
## The following objects are masked from 'package:stats':
##
## as.dist, dist
##
## The following object is masked from 'package:base':
##
## as.matrix
##
## Loading required package: dynamicTreeCut
## Loading required package: entropy
## Loading required package: tidyr## [1] "Clustering features: "
## [1] "D.T.condit" "D.T.use" "D.T.scratch" "D.T.new" "D.T.good"
## [6] "D.T.ipad" "D.T.screen" "D.T.great" "D.T.work" "D.T.excel"
## [11] "D.P.http" "D.P.mini" "D.P.air"
## [1] "glb_allobs_df Entropy: 5.5618"
## [1] "glb_allobs_df$prdline.my Entropy: 4.7594 (85.5734 pct)"
## prdline.my 0.01 0.1 0.45 0.5 0.98 0.99 1 1.99 2.99 3.99 4.69 4.99 5
## 1 Unknown 1 0 0 0 0 25 1 0 1 1 1 1 2
## 2 iPad 1 3 0 0 0 0 26 6 0 0 0 0 0 0
## 3 iPad 2 10 2 0 0 0 38 4 0 0 0 0 0 1
## 4 iPad 3+ 6 0 0 1 0 27 4 0 0 0 0 0 1
## 5 iPadAir 1 0 0 0 0 31 9 1 0 0 0 0 0
## 6 iPadmini 3 0 0 0 1 34 5 0 0 0 0 0 2
## 7 iPadmini 2+ 3 0 1 0 0 20 2 0 0 0 0 0 0
## 5.65 7.99 8 8.99 9 9.5 9.95 9.99 10 10.99 14 14.49 14.99 15 17.75 19.5
## 1 1 1 0 1 0 0 1 2 1 0 1 1 1 3 1 0
## 2 0 0 0 0 0 2 1 2 2 0 0 0 2 2 0 0
## 3 0 0 0 0 0 0 0 7 0 0 0 0 0 2 0 0
## 4 0 2 1 0 0 0 0 4 4 0 0 0 0 0 0 0
## 5 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 2 2 1 0 0 0 0 0 1
## 7 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0
## 19.95 19.99 20 24.99 25 28 28.75 29.95 29.99 30 30.99 32.95 33 35 36.95
## 1 0 1 2 1 4 1 1 0 0 1 1 1 0 1 0
## 2 1 3 3 0 3 0 0 1 5 3 0 0 1 1 3
## 3 2 1 1 0 1 0 0 1 1 3 0 0 0 0 0
## 4 0 0 1 0 2 0 0 0 0 0 0 0 0 1 0
## 5 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0
## 6 0 1 3 0 3 0 0 0 1 2 0 0 0 0 0
## 7 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0
## 37.98 38.99 39 39.99 40 41 42 43.2 44.99 45 48 48.99 49 49.49 49.95
## 1 1 0 0 2 2 1 0 1 1 1 0 0 0 0 1
## 2 0 0 0 1 5 0 1 0 0 2 1 1 0 1 0
## 3 0 0 0 1 4 0 0 0 0 1 0 0 1 0 0
## 4 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 6 0 0 0 0 1 0 1 0 0 3 0 0 0 0 1
## 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 49.99 50 51.99 52.25 52.99 54.99 55 55.66 56 57.5 58 59 59.95 59.99 60
## 1 1 7 1 1 0 0 0 0 0 1 1 0 1 1 0
## 2 4 22 0 0 1 1 5 1 1 0 2 1 0 1 2
## 3 4 6 0 0 0 0 0 0 0 0 0 0 0 2 1
## 4 2 4 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 2 4 0 0 0 0 0 0 0 0 0 1 0 0 1
## 6 1 10 0 0 0 0 0 0 0 0 0 0 0 1 3
## 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 62 63 64.99 65 66.99 69 69.5 69.69 69.95 69.99 70 70.99 71 71.99 72 74
## 1 0 1 0 1 0 1 0 0 0 0 2 1 0 0 0 0
## 2 2 0 1 3 0 2 0 0 0 2 7 0 0 0 1 1
## 3 0 0 0 2 1 0 1 0 1 2 3 0 0 1 1 1
## 4 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 1 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 74.5 74.95 74.99 75 76 78 79 79.94 79.95 79.97 79.99 80 82.95 82.98
## 1 0 0 0 3 1 0 0 0 2 0 0 2 0 0
## 2 1 0 1 10 0 1 4 1 0 0 5 12 1 1
## 3 0 0 2 7 0 0 1 0 0 1 0 5 0 0
## 4 0 0 0 1 0 0 0 0 0 0 1 3 0 0
## 5 0 0 0 2 0 0 0 0 0 0 0 1 0 0
## 6 0 1 1 6 0 0 1 0 0 0 1 0 0 0
## 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 84.99 85 85.95 87 89 89.5 89.95 89.99 90 91 92 92.14 92.49 93 94.99 95
## 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0
## 2 3 3 1 0 3 1 2 0 11 1 1 2 0 1 1 8
## 3 0 3 0 0 2 0 1 3 3 0 1 0 1 0 0 2
## 4 0 1 0 0 0 0 0 2 0 0 0 0 0 0 0 1
## 5 0 0 0 0 0 0 0 2 3 0 0 0 0 0 0 0
## 6 1 2 0 0 1 0 0 6 2 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 96 97.5 98 99 99.46 99.75 99.94 99.95 99.97 99.98 99.99 100 101 102 104
## 1 0 0 0 3 1 0 0 0 0 0 2 9 0 0 0
## 2 1 0 1 5 0 0 1 0 0 0 3 10 2 1 0
## 3 1 1 0 8 0 0 0 0 0 0 9 13 0 0 1
## 4 0 0 0 5 0 1 0 3 0 2 7 10 0 0 0
## 5 0 0 0 1 0 0 0 1 0 0 4 6 0 0 0
## 6 0 0 0 7 0 0 0 2 0 0 11 15 0 0 0
## 7 0 0 0 5 0 0 0 0 1 0 2 4 0 0 0
## 104.7 104.99 105 106.95 107 108 109 109.98 109.99 110 111 111.5 112
## 1 0 0 0 0 0 2 0 0 1 1 0 0 0
## 2 1 2 4 0 0 0 1 1 0 4 0 0 0
## 3 0 0 0 1 0 0 0 0 1 1 1 1 1
## 4 0 0 1 0 2 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 1 0 1 1 0 0 1
## 7 0 0 0 0 0 0 0 0 0 0 0 0 0
## 112.99 113 114.48 114.94 114.99 115 116.33 118 118.84 118.95 119 119.88
## 1 0 0 1 0 0 0 1 0 1 1 0 0
## 2 1 0 0 1 0 2 0 0 0 0 1 0
## 3 0 0 0 0 0 1 0 0 0 0 0 0
## 4 0 0 0 0 0 2 0 0 0 0 0 1
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 1 0 0 2 2 0 1 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 1 0
## 119.95 119.98 119.99 120 120.02 121 124 124.95 124.99 125 127.95 127.99
## 1 0 0 1 3 0 0 0 0 1 1 1 0
## 2 0 0 3 1 1 0 0 2 1 2 0 0
## 3 1 0 2 3 0 1 1 0 0 8 0 1
## 4 0 0 1 1 0 0 0 0 0 4 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 1 4 0 0 0 0 0 0 3 0 0
## 7 0 0 0 3 0 0 0 0 0 0 0 0
## 128 129 129.95 129.99 130 134.34 134.61 134.95 135 137.95 139 139.5
## 1 0 0 0 0 0 0 0 0 0 1 1 0
## 2 0 0 1 2 1 0 0 0 0 0 0 0
## 3 2 0 3 3 4 1 0 1 2 0 3 1
## 4 3 1 0 0 0 0 1 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 1 0
## 6 0 1 1 1 4 0 0 0 1 0 1 0
## 7 0 0 0 1 1 0 0 0 0 0 0 0
## 139.98 139.99 140 141.09 142.25 142.49 144.5 144.95 144.99 145 145.49
## 1 0 1 0 0 0 0 0 0 0 1 0
## 2 0 0 0 0 0 0 0 0 0 1 0
## 3 1 0 4 1 1 1 0 0 2 2 1
## 4 0 1 2 0 0 0 1 0 0 0 0
## 5 0 0 0 0 0 0 0 1 0 0 0
## 6 0 2 1 0 0 0 0 0 1 1 0
## 7 0 0 0 0 0 0 0 0 0 1 0
## 146.99 147.59 147.72 149 149.59 149.95 149.97 149.98 149.99 150 150.87
## 1 0 0 0 0 0 0 0 1 4 10 1
## 2 0 0 0 1 0 1 0 1 1 3 0
## 3 1 1 1 3 0 3 2 0 9 16 0
## 4 0 0 0 0 0 0 0 1 4 11 0
## 5 0 0 0 1 0 0 0 0 2 1 0
## 6 0 0 0 4 1 1 0 0 3 20 0
## 7 0 0 0 2 0 1 0 0 0 1 0
## 150.99 152 153.95 153.99 154 154.99 155 155.99 157 158.99 159 159.93
## 1 0 0 0 0 0 1 1 0 0 0 0 0
## 2 0 0 0 0 0 0 1 0 0 0 0 0
## 3 2 1 1 1 3 0 4 0 1 1 0 1
## 4 0 0 0 0 0 0 0 1 0 0 1 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 2 0 0 0 0 0
## 7 0 0 0 0 0 0 1 0 0 0 0 0
## 159.94 159.95 159.99 160 160.57 162 164 164.99 165 167.38 168 169 169.95
## 1 0 0 2 0 0 0 0 0 2 1 1 0 0
## 2 0 1 0 0 0 0 0 0 2 0 0 0 1
## 3 1 1 3 6 0 2 1 4 5 0 0 3 0
## 4 0 0 1 1 0 0 0 0 1 0 0 0 0
## 5 0 0 0 1 0 0 0 0 0 0 0 0 0
## 6 0 1 6 3 1 0 0 0 2 0 1 0 0
## 7 0 0 0 2 0 0 0 0 0 0 0 0 0
## 169.98 169.99 170 171 171.95 172 173 174 174.95 174.99 175 176.27 177.99
## 1 0 2 1 0 0 0 0 1 0 0 2 0 1
## 2 0 0 1 0 0 0 0 0 0 1 2 0 0
## 3 1 2 2 1 0 2 1 0 0 4 11 0 0
## 4 0 0 0 0 0 0 0 0 1 0 4 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 1 1 0 1 0 0 0 0 2 6 1 0
## 7 0 0 0 0 0 0 0 0 0 0 5 0 0
## 178.99 179 179.95 179.96 179.99 180 181 182 182.77 184.5 184.95 184.99
## 1 0 0 0 0 1 1 0 0 1 0 0 0
## 2 0 0 0 0 0 3 0 0 0 0 0 0
## 3 0 5 2 0 4 7 0 1 0 0 0 1
## 4 0 1 1 0 2 2 0 0 0 1 1 0
## 5 0 0 0 0 1 0 0 0 0 0 0 1
## 6 1 3 0 1 3 1 1 0 0 0 0 1
## 7 0 0 0 0 0 3 0 0 0 0 0 0
## 185 185.49 186 187 187.5 187.89 187.99 188 188.88 188.99 189 189.85
## 1 2 0 1 0 0 0 0 1 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 1 1
## 4 4 0 0 0 3 0 0 0 0 1 1 0
## 5 1 0 0 1 0 0 0 0 0 0 0 0
## 6 1 1 0 0 0 1 0 0 1 0 0 0
## 7 1 0 0 0 0 0 2 0 0 0 0 0
## 189.95 189.99 190 190.45 190.99 193 193.15 194 194.29 194.85 194.95 195
## 1 0 0 0 0 1 0 1 0 0 0 0 1
## 2 0 0 0 1 0 0 0 0 0 0 0 0
## 3 1 4 1 1 0 1 0 1 0 1 1 1
## 4 0 1 1 0 0 0 0 0 0 0 0 2
## 5 0 1 0 0 0 0 0 0 0 0 0 0
## 6 0 3 2 0 0 0 0 0 1 0 0 1
## 7 0 0 0 0 0 0 0 0 0 0 0 1
## 196 196.79 197.97 198 198.98 199 199.69 199.97 199.99 200 200.29 201.99
## 1 0 1 0 0 0 3 0 1 2 3 0 0
## 2 0 0 0 1 0 0 0 0 1 1 0 0
## 3 0 0 0 0 1 1 0 0 10 8 0 0
## 4 1 0 0 0 0 5 1 0 11 16 1 0
## 5 0 0 0 0 0 2 0 0 7 8 0 0
## 6 0 0 0 1 0 6 0 1 9 8 0 0
## 7 0 0 1 0 0 3 0 0 1 5 0 1
## 204 204.95 205 208 208.99 209 209.85 209.9 209.98 209.99 210 210.99
## 1 0 0 0 0 0 0 0 0 0 0 1 0
## 2 0 0 0 0 0 0 0 1 0 0 0 0
## 3 2 1 0 0 0 1 0 0 0 0 1 0
## 4 1 0 0 0 0 2 0 0 0 2 0 1
## 5 0 0 0 0 0 2 0 0 0 0 0 0
## 6 0 0 1 1 1 1 1 0 0 1 3 0
## 7 0 0 0 0 0 0 0 0 1 0 1 0
## 211.5 211.95 212.99 214.95 214.98 214.99 215 215.99 217 218 219 219.85
## 1 0 0 0 1 0 0 1 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 1 1 0 0 0 1 1 0 0 0 1
## 4 0 0 0 0 0 1 3 0 0 1 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 1 0 1 0 1 0 2 1 0 0 1 0
## 7 0 0 0 0 0 0 1 0 1 0 0 0
## 219.95 219.99 220 222.72 223 224 224.98 224.99 225 227 227.88 227.95
## 1 0 0 1 0 0 1 0 0 1 0 0 0
## 2 0 0 1 0 0 0 0 0 1 1 0 0
## 3 0 0 2 0 0 0 0 0 1 0 0 1
## 4 1 5 6 0 0 0 1 1 8 0 0 0
## 5 0 0 0 0 0 0 0 0 1 0 0 0
## 6 0 2 1 0 0 0 0 0 2 0 1 0
## 7 0 0 0 1 1 0 0 0 6 0 0 0
## 228.59 228.88 229 229.95 229.97 229.98 229.99 230 232.99 234 234.99 235
## 1 0 0 1 1 0 0 1 1 1 0 0 0
## 2 0 0 1 0 1 0 1 0 0 0 0 1
## 3 1 0 0 0 0 0 1 0 0 0 1 1
## 4 0 1 2 0 0 1 2 0 0 1 0 1
## 5 0 0 4 0 0 1 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 2 0 0 0 1
## 7 0 0 1 0 0 0 0 2 0 0 0 3
## 235.99 237 237.99 238 238.8 239 239.88 239.95 239.99 240 241.88 242
## 1 0 0 0 0 0 0 0 0 1 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 1 0 0 1 0 0 0
## 4 1 0 1 1 0 1 2 1 2 2 0 0
## 5 0 0 0 0 0 0 0 0 0 1 0 1
## 6 0 0 0 0 0 1 0 0 2 1 1 0
## 7 0 1 0 0 1 1 0 0 1 0 0 0
## 244.95 244.96 244.97 245 245.19 246 248 248.18 249 249.59 249.95 249.97
## 1 0 0 0 0 0 0 0 0 3 0 0 0
## 2 0 0 0 1 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 1 3
## 4 1 1 0 0 1 1 1 0 2 1 1 1
## 5 0 0 0 2 0 0 0 0 1 0 0 0
## 6 0 0 1 0 0 0 0 0 2 0 1 0
## 7 0 0 0 1 0 0 0 1 3 0 0 0
## 249.98 249.99 250 252.88 252.99 254.99 255 256.24 257.83 258.88 258.98
## 1 0 3 4 0 0 0 1 0 0 0 0
## 2 0 0 2 0 0 0 0 0 0 0 0
## 3 0 0 4 0 0 0 1 0 0 0 0
## 4 0 12 15 0 1 1 2 0 0 0 0
## 5 2 1 9 0 0 0 1 1 1 0 0
## 6 0 4 5 1 0 0 1 0 0 1 2
## 7 0 0 6 0 0 0 0 0 0 0 0
## 259 259.95 259.99 260 261.99 263.99 264.95 264.99 265 265.99 266.05 269
## 1 1 0 1 1 0 0 0 0 1 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 2 0 3 1 1 1 1 1 2 0 0 0
## 5 0 0 1 2 0 0 0 0 2 0 1 0
## 6 1 0 3 1 0 0 0 0 1 1 0 0
## 7 0 1 0 1 0 0 0 1 0 0 0 2
## 269.85 269.94 269.95 269.99 270 270.99 271 274 274.99 275 276.99 279
## 1 0 0 0 0 0 0 0 0 0 1 1 1
## 2 0 0 0 1 0 0 0 0 0 1 0 0
## 3 0 1 0 0 0 0 0 0 0 3 0 1
## 4 0 0 1 2 1 0 0 0 0 7 1 0
## 5 1 0 0 0 0 1 0 1 1 1 0 2
## 6 0 0 0 0 0 0 1 0 0 4 0 1
## 7 0 0 0 0 0 0 0 0 0 2 0 0
## 279.5 279.95 279.99 280 280.99 284 284.99 285 288 289 289.79 289.95
## 1 0 0 0 2 0 0 0 1 0 1 0 0
## 2 0 2 0 0 0 0 0 0 0 0 0 1
## 3 0 0 1 1 0 0 0 0 1 0 0 0
## 4 1 0 6 3 1 1 0 2 0 1 0 0
## 5 0 0 4 2 0 0 0 0 1 0 1 0
## 6 1 0 1 2 0 0 0 2 0 1 0 0
## 7 0 0 1 2 0 0 2 3 0 2 0 1
## 289.98 289.99 290 291.99 292.5 294.99 295 295.95 298 298.97 299 299.95
## 1 0 0 0 0 0 0 1 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 1 0 0 0 1 0 0 0 1 0 0
## 4 1 1 1 1 0 0 3 0 1 0 6 1
## 5 0 0 3 0 1 1 1 0 0 0 3 0
## 6 0 1 3 0 0 0 1 0 1 0 0 1
## 7 0 0 1 0 0 0 1 1 0 0 4 0
## 299.98 299.99 300 303.67 303.99 304.89 305 308 309.95 309.98 309.99 310
## 1 0 3 8 0 0 0 0 0 0 1 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 3 0 0 0 0 0 0 0 0 1
## 4 0 3 9 0 1 1 2 0 0 0 2 2
## 5 1 4 11 0 0 0 1 0 0 0 0 2
## 6 0 2 4 0 0 0 0 0 0 0 0 1
## 7 0 3 7 1 0 0 0 1 1 0 0 1
## 314.99 315 318 319 319.85 319.95 319.98 319.99 320 320.99 322.99 324.9
## 1 0 0 0 3 0 0 0 2 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 1 0 0 0 0 0 0 0 0 0 0
## 4 2 2 0 0 0 1 0 1 3 0 0 1
## 5 0 0 1 2 1 2 0 3 4 2 1 0
## 6 0 1 0 0 0 0 0 0 1 0 0 0
## 7 0 2 0 0 0 0 1 1 0 0 0 0
## 324.99 325 327.58 329 329.99 330 331.99 332.5 334 334.95 334.99 335 339
## 1 0 1 0 0 1 0 0 0 0 0 1 0 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 1 0 0 0 0 0 0 0
## 4 2 2 0 1 0 0 0 0 0 0 0 0 2
## 5 1 4 0 0 4 0 0 0 0 0 1 0 3
## 6 0 0 0 0 0 0 0 0 0 1 0 0 0
## 7 0 6 1 3 5 0 1 1 1 0 0 1 2
## 339.5 339.98 339.99 340 344 344.95 345 346 347 347.24 348.6 349 349.95
## 1 0 0 0 1 0 0 0 0 1 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 1 0 0 0 0 0 0 0 0 0
## 4 0 0 0 1 2 0 1 0 0 0 0 0 0
## 5 0 0 1 0 0 1 0 1 0 1 0 3 1
## 6 0 0 1 0 0 0 0 0 0 0 1 0 0
## 7 1 1 1 1 0 0 2 0 0 0 0 2 1
## 349.99 350 350.25 351 358.24 358.87 359 359.99 360 360.24 367.97 369.99
## 1 1 3 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 1 2 0 0 0 0 0 0 0 0 0 0
## 4 1 3 0 0 0 0 0 0 0 0 2 0
## 5 4 9 0 0 1 0 0 1 1 1 0 2
## 6 1 5 0 1 0 1 0 0 0 0 0 0
## 7 3 12 1 0 0 0 1 1 0 0 0 0
## 370 374.95 374.99 375 375.99 376 379 379.95 379.99 380 384.99 385 387
## 1 0 0 0 2 0 0 0 0 0 1 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 2 0 0 0 0 0 1 0 0 0
## 5 1 2 1 3 1 0 3 0 4 2 1 0 0
## 6 1 0 0 1 0 0 0 0 1 0 0 1 0
## 7 1 0 0 5 0 1 0 1 2 2 0 2 1
## 387.45 388.3 388.99 389 389.99 393 394.99 395 396 397.75 398.99 399
## 1 0 0 0 1 0 0 0 1 0 0 0 2
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 1 0 1 1 0 0 0
## 4 0 0 0 0 0 0 0 1 0 0 0 0
## 5 0 0 1 1 6 0 1 1 0 0 2 4
## 6 1 1 0 0 0 0 0 0 0 1 1 0
## 7 0 0 0 0 2 0 1 1 0 0 0 1
## 399.94 399.95 399.99 400 404.99 406 408 408.6 409.99 410 415 417 419
## 1 0 0 0 0 0 0 0 1 0 0 1 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 1 0 0 0 0 0 1 0
## 4 0 0 1 5 0 0 0 0 0 1 0 0 0
## 5 1 2 6 10 1 0 1 0 0 1 3 0 2
## 6 0 0 1 3 0 0 0 0 1 0 0 0 0
## 7 0 0 5 5 0 0 0 0 0 0 0 0 0
## 419.95 419.99 420 424.55 424.65 424.95 424.99 425 425.99 426.3 426.99
## 1 0 1 0 0 0 0 0 0 1 1 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 1 0 0 0
## 4 0 1 0 0 0 0 0 1 0 0 0
## 5 0 0 1 1 1 1 1 3 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## 7 1 1 1 0 0 0 0 1 0 0 1
## 429 429.95 429.99 430 438 438.99 439 439.98 439.99 440 443.09 444.99 445
## 1 0 0 0 0 0 0 0 1 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 1
## 5 1 1 2 2 1 1 1 1 4 1 1 1 1
## 6 1 0 1 0 0 0 0 0 0 0 0 0 0
## 7 0 0 1 1 0 0 0 0 1 0 0 0 0
## 445.95 449 449.95 449.99 450 454 454.68 455 458 459 459.95 459.99 460
## 1 0 0 0 0 2 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 2 0 0 0 0 0 0 0 0
## 5 0 2 0 3 8 1 1 1 0 1 1 1 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## 7 1 1 1 3 2 0 0 0 1 0 0 1 2
## 462.89 463.26 465.99 469 469.99 470 473.6 475 479.99 480 485 489.99 490
## 1 0 0 0 0 0 1 1 0 0 1 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 1 0 0 0 0 0 0 1 0 0 0 0
## 5 1 0 2 0 2 0 0 2 0 3 1 1 1
## 6 0 0 0 0 0 0 0 1 0 0 0 0 0
## 7 0 0 0 1 1 0 0 1 0 0 1 0 0
## 490.95 494.5 495.49 495.99 498.88 499 499.95 499.99 500 509 509.99 510
## 1 0 0 0 0 0 0 0 0 2 0 0 0
## 2 0 0 0 0 0 1 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 1 0 0 0 0 0 0 2 0 0 0
## 5 1 0 1 1 0 4 1 5 8 1 1 1
## 6 0 0 0 0 0 0 0 1 0 0 0 0
## 7 0 0 0 0 2 2 0 5 3 1 0 1
## 514.95 515 517.89 520 520.9 525 528 529 529.95 529.99 535 539.95 540
## 1 0 0 0 0 1 0 0 0 0 0 1 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 1 0 0 0 0 0 0 0
## 4 0 0 0 1 0 0 0 0 0 0 0 0 1
## 5 1 1 1 1 0 3 1 1 1 1 0 1 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 1 0 0 0 1 0 0 0
## 544.49 549 549.9 549.95 549.99 550 554.77 558.17 559 559.99 560 561.53
## 1 0 0 0 0 0 1 1 0 0 0 0 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 1 0 0 0 0 0 0 0 0 1 0 0
## 5 0 2 1 1 5 10 0 1 1 2 3 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 1 2 0 0 0 1 0 0
## 565.95 569 573.74 575 579.99 585.99 588.18 589 589.99 590 595 598.98 599
## 1 0 0 0 0 0 0 0 0 0 1 1 0 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 1 0 0 0 2 0 0 0 0 0 0
## 5 1 0 0 2 3 1 0 1 1 1 1 1 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## 7 0 1 0 2 1 0 0 0 0 0 1 0 0
## 599.99 600 609.99 614.99 615.99 619 619.99 624.99 625 629 630 634.99 639
## 1 2 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 1 0 0 0 0 0 0 0 0 0 0 0
## 5 1 1 0 1 1 1 1 1 1 1 1 1 2
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## 7 5 0 1 1 0 0 0 0 0 0 0 0 0
## 639.99 640 645 645.99 648 649.95 649.99 650 659.49 660 670 675 679.95
## 1 1 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 2 1 0 0 0 0 0
## 5 2 0 1 1 2 1 1 3 1 1 1 1 1
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## 7 1 0 0 0 0 0 0 1 0 0 0 0 0
## 679.99 680 689.99 695 699 699.95 700 710 720.12 729.99 730 740 749
## 1 1 0 0 0 0 0 2 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 1 0 0 0 0 0 0
## 4 0 0 0 1 0 1 0 0 0 0 0 0 0
## 5 1 1 0 0 1 0 0 1 0 2 1 1 2
## 6 0 0 0 0 0 0 0 0 1 0 0 0 0
## 7 0 0 1 0 0 0 0 0 0 3 0 0 0
## 749.95 749.99 750 785 789 789.99 795 795.99 798 799 799.99 800 820
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 2 1 2 1 1 1 1 1 1 1 0 2 1
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 1 0 0
## 829.99 879.99 899.99 900 939 948.98 999 999.99 .entropy .knt
## 1 0 0 0 0 0 0 0 0 4.811470 285
## 2 0 0 0 0 0 0 0 0 4.359548 314
## 3 0 0 0 0 0 0 0 0 4.700760 442
## 4 0 0 0 0 0 0 0 0 4.798635 434
## 5 1 1 1 1 1 0 0 0 5.114880 490
## 6 0 0 0 0 0 0 1 1 4.663055 393
## 7 0 0 0 0 0 1 0 0 4.703751 299
## [1] "Category: Unknown"
## [1] "max distance(1.0000) pair:"
## UniqueID startprice prdline.my
## 5 10005 199.99 Unknown
## 130 10130 100.00 Unknown
## descr.my
## 5 Please feel free to buy. All product have been thoroughly inspected, cleaned and tested to be 100%
## 130 New - Open Box. Charger included.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 5 0 0 0 0.0000000 0 0 0
## 130 0 0 0 0.8180361 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 5 0 0 0 0 0 0
## 130 0 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID startprice prdline.my
## 1029 11029 108 Unknown
## 1077 11077 108 Unknown
## descr.my
## 1029 A device listed in near mint used cosmetic condition with light blemishes from use. Housing &
## 1077 A device listed in near mint used cosmetic condition with light blemishes from use. Housing &
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 1029 0.220126 0.5801286 0 0 0 0 0
## 1077 0.220126 0.5801286 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 1029 0 0 0 0 0 0
## 1077 0 0 0 0 0 0
## [1] "Category: iPad 1"
## [1] "max distance(1.0000) pair:"
## UniqueID startprice prdline.my
## 9 10009 0.99 iPad 1
## 13 10013 20.00 iPad 1
## descr.my
## 9
## 13 GOOD CONDITION. CLEAN ICLOUD. NO LOCKS. CLEAN IMEI. This tablet has been fully tested and works
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 9 0.000000 0 0 0 0.0000000 0 0
## 13 0.220126 0 0 0 0.3412301 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 9 0 0.000000 0 0 0 0
## 13 0 0.340566 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID startprice prdline.my
## 471 10471 110 iPad 1
## 1202 11202 119 iPad 1
## descr.my
## 471 Used Apple Ipad 64 gig 1st generation in Great working condition and 100% functional SIM card AT&T
## 1202 Used Apple Ipad 64 gig 1st generation in Great working condition and 100% functional SIM card AT&T
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 471 0.1862605 0.245439 0 0 0 0.2705847 0
## 1202 0.1862605 0.245439 0 0 0 0.2705847 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 471 0.3392152 0.2881712 0 0 0 0
## 1202 0.3392152 0.2881712 0 0 0 0
## [1] "Category: iPad 2"
## [1] "max distance(1.0000) pair:"
## UniqueID startprice prdline.my
## 1 10001 159.99 iPad 2
## 2 10002 0.99 iPad 2
## descr.my
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 1 0.8071287 0.0000000 0.0000000 0 0 1.172534 0.0000000
## 2 0.0000000 0.5801286 0.2923374 0 0 0.000000 0.3309884
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID startprice prdline.my
## 132 10132 119.99 iPad 2
## 2382 12384 189.95 iPad 2
## descr.my
## 132 Overall good condition. Some wear from use. Scratches/ scuffs/ nicks/ scrapes on unit housing back,
## 2382 Device is in GOOD used cosmetic condition with normal scratches & wear, engravement on the housing.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad
## 132 0.2017822 0.2658923 0.2679759 0 0.3127942 0
## 2382 0.2421386 0.3190707 0.3215711 0 0.3753531 0
## D.T.screen D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 132 0 0 0 0 0 0 0
## 2382 0 0 0 0 0 0 0
## [1] "Category: iPad 3+"
## [1] "max distance(1.0000) pair:"
## UniqueID startprice prdline.my
## 3 10003 199.99 iPad 3+
## 11 10011 199.99 iPad 3+
## descr.my
## 3
## 11 good condition, minor wear and tear on body some light scratches on screen. functions great.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 3 0.000000 0 0.0000000 0 0.0000000 0 0.0000000
## 11 0.220126 0 0.2923374 0 0.3412301 0 0.3309884
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 3 0.0000000 0 0 0 0 0
## 11 0.4008907 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID startprice prdline.my
## 40 10040 159.99 iPad 3+
## 1602 11603 0.99 iPad 3+
## descr.my
## 40 Item has been professionally tested and inspected. Tests show that all features work correctly. This
## 1602 Work fine iCloud lock
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 40 0 0 0 0 0 0 0
## 1602 0 0 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 40 0 0.4162473 0 0 0 0
## 1602 0 0.9365565 0 0 0 0
## [1] "Category: iPadAir"
## [1] "max distance(1.0000) pair:"
## UniqueID startprice prdline.my
## 16 10016 344.95 iPadAir
## 33 10033 299.98 iPadAir
## descr.my
## 16
## 33 We are selling good quality iPads that have been fully tested by an Apple Certified Technician. The
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 16 0 0 0 0 0.000000 0.0000000 0
## 33 0 0 0 0 0.417059 0.3908446 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 16 0 0 0 0 0 0
## 33 0 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID startprice prdline.my
## 44 10044 499.95 iPadAir
## 1233 11233 199.99 iPadAir
## descr.my
## 44 Open Box Units Grade A Condition. Units may contain minor cosmetic imperfections.
## 1233 MINT CONDITION!
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 44 0.220126 0 0 0 0 0 0
## 1233 1.210693 0 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 44 0 0 0 0 0 0
## 1233 0 0 0 0 0 0
## [1] "Category: iPadmini"
## [1] "max distance(1.0000) pair:"
## UniqueID startprice prdline.my
## 7 10007 100 iPadmini
## 76 10076 130 iPadmini
## descr.my
## 7
## 76 Works perfectly, NOT iCloud locked, 1 owner. It is in not in very good condition, but works
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 7 0.0000000 0 0 0 0.0000000 0 0
## 76 0.3026733 0 0 0 0.4691913 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 7 0 0.0000000 0 0 0 0
## 76 0 0.9365565 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID startprice prdline.my
## 491 10491 5 iPadmini
## 1753 11754 79 iPadmini
## descr.my
## 491 Cracked screen, flaw is shown in picture, everything is fully functional and
## 1753 Shows Apple ID locked, password required on activation screen. Unknown imei and storage space.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 491 0 0 0 0 0 0 0.4551091
## 1753 0 0 0 0 0 0 0.4045414
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 491 0 0 0 0 0 0
## 1753 0 0 0 0 0 0
## [1] "Category: iPadmini 2+"
## [1] "max distance(1.0000) pair:"
## UniqueID startprice prdline.my
## 4 10004 235.00 iPadmini 2+
## 18 10018 209.98 iPadmini 2+
## descr.my
## 4
## 18 We are selling good quality iPads that have been fully tested by an Apple Certified Technician. The
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 4 0 0 0 0 0.000000 0.0000000 0
## 18 0 0 0 0 0.417059 0.3908446 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 4 0 0 0 0 0 0
## 18 0 0 0 0 0 0
## [1] "min distance(0.0000) pair:"
## UniqueID startprice prdline.my descr.my D.T.condit D.T.use D.T.scratch
## 4 10004 235 iPadmini 2+ 0 0 0
## 6 10006 175 iPadmini 2+ 0 0 0
## D.T.new D.T.good D.T.ipad D.T.screen D.T.great D.T.work D.T.excel
## 4 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## D.P.http D.P.mini D.P.air
## 4 0 0 0
## 6 0 0 0
## [1] "glb_allobs_df$prdline.my$.clusterid Entropy: NA (NA pct)"
## prdline.my.clusterid 0.01 0.1 0.45 0.5 0.98 0.99 1 1.99 2.99 3.99 4.69
## 1 Unknown_1 NA NA NA NA NA 19 1 NA 1 1 1
## 2 Unknown_2 1 NA NA NA NA 3 NA NA NA NA NA
## 3 Unknown_3 NA NA NA NA NA 3 NA NA NA NA NA
## 4 iPad 1_1 2 NA NA NA NA 17 6 NA NA NA NA
## 5 iPad 1_2 NA NA NA NA NA 2 NA NA NA NA NA
## 6 iPad 1_3 1 NA NA NA NA 3 NA NA NA NA NA
## 7 iPad 1_4 NA NA NA NA NA 4 NA NA NA NA NA
## 8 iPad 2_1 4 2 NA NA NA 28 4 NA NA NA NA
## 9 iPad 2_2 3 NA NA NA NA 6 NA NA NA NA NA
## 10 iPad 2_3 NA NA NA NA NA 1 NA NA NA NA NA
## 11 iPad 2_4 2 NA NA NA NA 3 NA NA NA NA NA
## 12 iPad 2_5 1 NA NA NA NA NA NA NA NA NA NA
## 13 iPad 3+_1 3 NA NA 1 NA 18 3 NA NA NA NA
## 14 iPad 3+_2 1 NA NA NA NA 2 1 NA NA NA NA
## 15 iPad 3+_3 1 NA NA NA NA 4 NA NA NA NA NA
## 16 iPad 3+_4 1 NA NA NA NA 3 NA NA NA NA NA
## 17 iPadAir_1 1 NA NA NA NA 19 5 NA NA NA NA
## 18 iPadAir_2 NA NA NA NA NA 6 4 1 NA NA NA
## 19 iPadAir_3 NA NA NA NA NA 4 NA NA NA NA NA
## 20 iPadAir_4 NA NA NA NA NA 2 NA NA NA NA NA
## 21 iPadmini 2+_1 2 NA 1 NA NA 12 1 NA NA NA NA
## 22 iPadmini 2+_2 NA NA NA NA NA 3 NA NA NA NA NA
## 23 iPadmini 2+_3 1 NA NA NA NA 5 1 NA NA NA NA
## 24 iPadmini_1 2 NA NA NA NA 23 3 NA NA NA NA
## 25 iPadmini_2 NA NA NA NA 1 4 NA NA NA NA NA
## 26 iPadmini_3 NA NA NA NA NA 2 1 NA NA NA NA
## 27 iPadmini_4 NA NA NA NA NA 4 NA NA NA NA NA
## 28 iPadmini_5 1 NA NA NA NA 1 1 NA NA NA NA
## 4.99 5 5.65 7.99 8 8.99 9 9.5 9.95 9.99 10 10.99 14 14.49 14.99 15
## 1 1 2 1 1 NA 1 NA NA 1 1 1 NA 1 1 1 3
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA 1 NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA 1 1 NA 1 NA NA NA 2 2
## 5 NA NA NA NA NA NA NA NA NA 2 1 NA NA NA NA NA
## 6 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA 1 NA NA NA NA NA NA NA 2 NA NA NA NA NA 2
## 9 NA NA NA NA NA NA NA NA NA 5 NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA 1 NA 1 NA NA NA NA NA 3 4 NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA 1 1 NA NA NA NA 1 NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA 1 NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA 1 NA 1 NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA 1 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA
## 25 NA 1 NA NA NA NA NA NA NA NA 1 1 NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA 1 NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA 1 NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17.75 19.5 19.95 19.99 20 24.99 25 28 28.75 29.95 29.99 30 30.99 32.95
## 1 1 NA NA 1 NA NA 4 1 1 NA NA 1 NA 1
## 2 NA NA NA NA 1 1 NA NA NA NA NA NA 1 NA
## 3 NA NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 4 NA NA 1 3 1 NA 2 NA NA 1 5 3 NA NA
## 5 NA NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA 1 NA NA NA NA NA NA NA
## 7 NA NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 8 NA NA 1 NA 1 NA 1 NA NA 1 1 2 NA NA
## 9 NA NA NA 1 NA NA NA NA NA NA NA 1 NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA 1 NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA 1 NA 1 NA NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA 1 NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA NA 1 1 1 NA NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA 2 NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA 1 NA 1 1 NA 2 NA NA NA 1 2 NA NA
## 25 NA NA NA NA 2 NA 1 NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 33 35 36.95 37.98 38.99 39 39.99 40 41 42 43.2 44.99 45 48 48.99 49
## 1 NA 1 NA 1 NA NA 2 2 1 NA 1 NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA 1 1 NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 1 1 3 NA NA NA NA 3 NA 1 NA NA 1 1 NA NA
## 5 NA NA NA NA NA NA 1 1 NA NA NA NA 1 NA NA NA
## 6 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA 1 NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA 4 NA NA NA NA 1 NA NA 1
## 9 NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA 1 NA NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA 1 NA NA NA NA NA NA NA 1 NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA 1 NA 1 NA NA 3 NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 49.49 49.95 49.99 50 51.99 52.25 52.99 54.99 55 55.66 56 57.5 58 59
## 1 NA NA NA 3 1 1 NA NA NA NA NA 1 1 NA
## 2 NA NA NA 3 NA NA NA NA NA NA NA NA NA NA
## 3 NA 1 1 1 NA NA NA NA NA NA NA NA NA NA
## 4 1 NA 1 19 NA NA 1 1 4 NA NA NA 2 NA
## 5 NA NA NA 1 NA NA NA NA NA 1 1 NA NA NA
## 6 NA NA 2 2 NA NA NA NA 1 NA NA NA NA NA
## 7 NA NA 1 NA NA NA NA NA NA NA NA NA NA 1
## 8 NA NA 2 3 NA NA NA NA NA NA NA NA NA NA
## 9 NA NA 2 2 NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA 2 2 NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA 1 4 NA NA NA NA NA NA NA NA NA NA
## 18 NA NA 1 NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA 1
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA 1 NA NA NA NA NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA 6 NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA 2 NA NA NA NA NA NA NA NA NA NA
## 26 NA 1 NA 2 NA NA NA NA NA NA NA NA NA NA
## 27 NA NA 1 NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 59.95 59.99 60 62 63 64.99 65 66.99 69 69.5 69.69 69.95 69.99 70 70.99
## 1 1 1 NA NA 1 NA NA NA 1 NA NA NA NA 1 NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA 1 1
## 3 NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA
## 4 NA 1 NA 2 NA NA 2 NA NA NA NA NA 2 6 NA
## 5 NA NA 1 NA NA NA 1 NA 1 NA NA NA NA NA NA
## 6 NA NA NA NA NA 1 NA NA 1 NA NA NA NA 1 NA
## 7 NA NA 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA 2 NA NA NA NA 1 NA NA NA NA 1 1 3 NA
## 9 NA NA 1 NA NA NA 1 1 NA 1 NA NA 1 NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA 1 NA NA NA 1 NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA 1 3 1 NA NA NA NA NA NA NA NA NA 1 NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA 1 NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 71 71.99 72 74 74.5 74.95 74.99 75 76 78 79 79.94 79.95 79.97 79.99 80
## 1 NA NA NA NA NA NA NA 2 NA NA NA NA 2 NA NA 1
## 2 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA 1 NA NA NA NA NA NA 1
## 4 NA NA 1 NA NA NA 1 9 NA NA 3 NA NA NA NA 7
## 5 NA NA NA 1 1 NA NA NA NA NA 1 NA NA NA 2 3
## 6 NA NA NA NA NA NA NA 1 NA NA NA 1 NA NA NA 2
## 7 NA NA NA NA NA NA NA NA NA 1 NA NA NA NA 3 NA
## 8 NA 1 1 1 NA NA 1 6 NA NA 1 NA NA 1 NA 5
## 9 NA NA NA NA NA NA 1 1 NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 2
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1 NA
## 16 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA
## 17 NA NA NA NA NA NA NA 2 NA NA NA NA NA NA NA 1
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA NA NA NA 1 NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA 1 NA 3 NA NA NA NA NA NA 1 NA
## 25 NA NA NA NA NA NA NA 1 NA NA 1 NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA 1 1 NA NA NA NA NA NA NA NA
## 82.95 82.98 84.99 85 85.95 87 89 89.5 89.95 89.99 90 91 92 92.14 92.49
## 1 NA NA NA NA NA 1 NA NA NA NA NA 1 NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA 1 NA NA NA 1 NA NA NA NA
## 4 1 1 3 1 1 NA 2 NA NA NA 7 NA NA 2 NA
## 5 NA NA NA 2 NA NA 1 NA NA NA 1 1 1 NA NA
## 6 NA NA NA NA NA NA NA NA 1 NA 1 NA NA NA NA
## 7 NA NA NA NA NA NA NA 1 1 NA 2 NA NA NA NA
## 8 NA NA NA 2 NA NA 1 NA 1 1 3 NA NA NA 1
## 9 NA NA NA 1 NA NA 1 NA NA 2 NA NA 1 NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA 1 NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA 1 NA NA NA NA NA
## 15 NA NA NA 1 NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA NA NA NA NA NA NA 1 2 NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA 1 1 NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA 1 1 NA NA 1 NA NA 5 2 NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA 1 NA NA NA NA NA 1 NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 93 94.99 95 96 97.5 98 99 99.46 99.75 99.94 99.95 99.97 99.98 99.99 100
## 1 NA NA NA NA NA NA 1 1 NA NA NA NA NA NA 5
## 2 NA NA NA NA NA NA 1 NA NA NA NA NA NA 2 3
## 3 NA NA NA NA NA NA 1 NA NA NA NA NA NA NA 1
## 4 NA 1 4 NA NA 1 1 NA NA NA NA NA NA 3 9
## 5 NA NA 2 1 NA NA 3 NA NA 1 NA NA NA NA 1
## 6 NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA
## 7 1 NA 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA 1 1 NA NA 6 NA NA NA NA NA NA 7 9
## 9 NA NA 1 NA 1 NA 1 NA NA NA NA NA NA 1 1
## 10 NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA 1 3
## 13 NA NA 1 NA NA NA 4 NA 1 NA 3 NA 2 4 6
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA 2 1
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA 1 1
## 16 NA NA NA NA NA NA 1 NA NA NA NA NA NA NA 2
## 17 NA NA NA NA NA NA 1 NA NA NA 1 NA NA 2 2
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA 2 3
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1
## 21 NA NA NA NA NA NA 5 NA NA NA NA NA NA 1 3
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1
## 23 NA NA NA NA NA NA NA NA NA NA NA 1 NA 1 NA
## 24 NA NA NA NA NA NA 5 NA NA NA 2 NA NA 5 8
## 25 NA NA NA NA NA NA 1 NA NA NA NA NA NA 4 NA
## 26 NA NA NA NA NA NA 1 NA NA NA NA NA NA NA 3
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA 1 3
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA 1 1
## 101 102 104 104.7 104.99 105 106.95 107 108 109 109.98 109.99 110 111
## 1 NA NA NA NA NA NA NA NA NA NA NA 1 NA NA
## 2 NA NA NA NA NA NA NA NA 2 NA NA NA 1 NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA 1 NA 1 NA NA NA NA 1 NA 1 NA
## 5 2 NA NA NA 1 NA NA NA NA NA NA NA 1 NA
## 6 NA NA NA NA NA 3 NA NA NA 1 NA NA 2 NA
## 7 NA 1 NA NA 1 NA NA NA NA NA NA NA NA NA
## 8 NA NA 1 NA NA NA NA NA NA NA NA 1 NA 1
## 9 NA NA NA NA NA NA 1 NA NA NA NA NA 1 NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA NA 1 NA 1 NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA 1 NA 1 1 NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 111.5 112 112.99 113 114.48 114.94 114.99 115 116.33 118 118.84 118.95
## 1 NA NA NA NA 1 NA NA NA 1 NA 1 1
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA 1 NA 1 NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA 1 NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA 1 NA NA NA NA
## 8 NA 1 NA NA NA NA NA 1 NA NA NA NA
## 9 1 NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA 1 NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA 1 NA NA NA NA
## 17 NA NA NA NA NA NA NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA 1 NA NA 1 2 NA 1 NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA 1 NA NA NA NA 1 NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 119 119.88 119.95 119.98 119.99 120 120.02 121 124 124.95 124.99 125
## 1 NA NA NA NA 1 2 NA NA NA NA NA 1
## 2 NA NA NA NA NA 1 NA NA NA NA 1 NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA 1 1 1 NA NA 2 1 1
## 5 NA NA NA NA 1 NA NA NA NA NA NA 1
## 6 1 NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA 1 NA NA NA NA NA NA NA
## 8 NA NA NA NA 1 3 NA 1 1 NA NA 5
## 9 NA NA 1 NA NA NA NA NA NA NA NA 3
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA 1 NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA 1 NA NA NA 1 NA NA NA NA NA 4
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA 1 NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA NA NA NA NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 1 NA NA NA NA 3 NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA 4 NA NA NA NA NA NA 3
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA 1 NA NA NA NA NA NA NA NA
## 127.95 127.99 128 129 129.95 129.99 130 134.34 134.61 134.95 135 137.95
## 1 1 NA NA NA NA NA NA NA NA NA NA 1
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA 1 NA NA NA NA NA NA
## 5 NA NA NA NA NA 1 NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA 1 NA 1 NA NA NA NA NA
## 8 NA 1 1 NA NA 2 3 NA NA NA 2 NA
## 9 NA NA NA NA 2 NA 1 1 NA 1 NA NA
## 10 NA NA NA NA NA 1 NA NA NA NA NA NA
## 11 NA NA 1 NA 1 NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA 1 NA NA NA NA 1 NA NA NA
## 14 NA NA 3 NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA NA NA NA NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA 1 NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA 1 NA NA NA NA NA NA
## 24 NA NA NA 1 NA 1 1 NA NA NA 1 NA
## 25 NA NA NA NA 1 NA 1 NA NA NA NA NA
## 26 NA NA NA NA NA NA 2 NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 139 139.5 139.98 139.99 140 141.09 142.25 142.49 144.5 144.95 144.99
## 1 NA NA NA 1 NA NA NA NA NA NA NA
## 2 1 NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA
## 8 2 1 NA NA 2 NA NA 1 NA NA 1
## 9 1 NA NA NA 1 NA 1 NA NA NA NA
## 10 NA NA 1 NA NA 1 NA NA NA NA 1
## 11 NA NA NA NA 1 NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA 1 2 NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA 1 NA NA
## 17 1 NA NA NA NA NA NA NA NA 1 NA
## 18 NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA
## 24 1 NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA 2 NA NA NA NA NA NA 1
## 26 NA NA NA NA 1 NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA
## 145 145.49 146.99 147.59 147.72 149 149.59 149.95 149.97 149.98 149.99
## 1 1 NA NA NA NA NA NA NA NA 1 1
## 2 NA NA NA NA NA NA NA NA NA NA 2
## 3 NA NA NA NA NA NA NA NA NA NA 1
## 4 1 NA NA NA NA 1 NA 1 NA NA 1
## 5 NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA 1 NA
## 8 NA NA NA NA NA 1 NA 2 1 NA 2
## 9 2 NA 1 NA NA 1 NA NA NA NA 2
## 10 NA NA NA NA 1 NA NA NA 1 NA 3
## 11 NA 1 NA 1 NA 1 NA NA NA NA 2
## 12 NA NA NA NA NA NA NA 1 NA NA NA
## 13 NA NA NA NA NA NA NA NA NA 1 2
## 14 NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA 1
## 16 NA NA NA NA NA NA NA NA NA NA 1
## 17 NA NA NA NA NA 1 NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA 1
## 19 NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA 1
## 21 NA NA NA NA NA 2 NA 1 NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA
## 23 1 NA NA NA NA NA NA NA NA NA NA
## 24 1 NA NA NA NA 2 1 NA NA NA 1
## 25 NA NA NA NA NA 1 NA NA NA NA NA
## 26 NA NA NA NA NA NA NA 1 NA NA NA
## 27 NA NA NA NA NA 1 NA NA NA NA 2
## 28 NA NA NA NA NA NA NA NA NA NA NA
## 150 150.87 150.99 152 153.95 153.99 154 154.99 155 155.99 157 158.99
## 1 7 1 NA NA NA NA NA NA NA NA NA NA
## 2 2 NA NA NA NA NA NA 1 1 NA NA NA
## 3 1 NA NA NA NA NA NA NA NA NA NA NA
## 4 2 NA NA NA NA NA NA NA 1 NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 1 NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 13 NA 1 1 NA NA NA NA 2 NA 1 NA
## 9 2 NA 1 NA NA 1 2 NA 1 NA NA NA
## 10 1 NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA 1 NA NA NA NA NA
## 12 NA NA NA NA 1 NA NA NA 1 NA NA 1
## 13 9 NA NA NA NA NA NA NA NA 1 NA NA
## 14 1 NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA
## 16 1 NA NA NA NA NA NA NA NA NA NA NA
## 17 1 NA NA NA NA NA NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA NA NA NA
## 22 1 NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA 1 NA NA NA
## 24 16 NA NA NA NA NA NA NA 2 NA NA NA
## 25 1 NA NA NA NA NA NA NA NA NA NA NA
## 26 2 NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 1 NA NA NA NA NA NA NA NA NA NA NA
## 159 159.93 159.94 159.95 159.99 160 160.57 162 164 164.99 165 167.38
## 1 NA NA NA NA NA NA NA NA NA NA NA 1
## 2 NA NA NA NA 1 NA NA NA NA NA 2 NA
## 3 NA NA NA NA 1 NA NA NA NA NA NA NA
## 4 NA NA NA 1 NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA 2 NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA 1 NA 1 4 NA NA 1 1 4 NA
## 9 NA 1 NA 1 1 1 NA NA NA 3 1 NA
## 10 NA NA NA NA NA 1 NA 2 NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA 1 NA NA NA NA NA NA NA
## 13 1 NA NA NA NA 1 NA NA NA NA 1 NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA 1 NA NA NA NA NA NA NA
## 17 NA NA NA NA NA 1 NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA 2 NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA 1 4 2 NA NA NA NA 1 NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA 1 NA 1 NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA 1 NA
## 28 NA NA NA NA 1 1 NA NA NA NA NA NA
## 168 169 169.95 169.98 169.99 170 171 171.95 172 173 174 174.95 174.99
## 1 1 NA NA NA 1 NA NA NA NA NA 1 NA NA
## 2 NA NA NA NA 1 1 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA 1 NA NA NA NA NA NA NA NA NA 1
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA 1 NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA 1 2 NA NA 1 NA NA NA 2
## 9 NA NA NA 1 1 NA NA NA NA NA NA NA 1
## 10 NA NA NA NA NA NA 1 NA 1 NA NA NA 1
## 11 NA 3 NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA 1 NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA 1 NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 1 NA NA NA NA NA NA NA NA NA NA NA 2
## 25 NA NA NA NA NA 1 NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA 1 NA NA 1 NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 175 176.27 177.99 178.99 179 179.95 179.96 179.99 180 181 182 182.77
## 1 1 NA 1 NA NA NA NA 1 1 NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA 1
## 3 1 NA NA NA NA NA NA NA NA NA NA NA
## 4 1 NA NA NA NA NA NA NA 3 NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 1 NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 6 NA NA NA 2 1 NA 2 5 NA NA NA
## 9 3 NA NA NA NA 1 NA NA 1 NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA 1 NA
## 11 1 NA NA NA 2 NA NA 2 NA NA NA NA
## 12 1 NA NA NA 1 NA NA NA 1 NA NA NA
## 13 4 NA NA NA 1 1 NA 2 2 NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA NA NA NA NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA 1 NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 5 NA NA NA NA NA NA NA 3 NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 5 1 NA 1 NA NA 1 1 1 1 NA NA
## 25 NA NA NA NA 1 NA NA 1 NA NA NA NA
## 26 NA NA NA NA NA NA NA 1 NA NA NA NA
## 27 1 NA NA NA 2 NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 184.5 184.95 184.99 185 185.49 186 187 187.5 187.89 187.99 188 188.88
## 1 NA NA NA 1 NA NA NA NA NA NA NA NA
## 2 NA NA NA 1 NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA 1 NA NA NA NA 1 NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA 1 NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA 3 NA NA NA NA NA NA NA NA
## 14 NA 1 NA 1 NA NA NA NA NA NA NA NA
## 15 1 NA NA NA NA NA NA 3 NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA NA NA NA NA NA NA NA NA NA
## 18 NA NA 1 1 NA NA 1 NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA 1 NA NA NA NA NA 2 NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA 1 NA NA NA NA NA NA NA NA
## 25 NA NA 1 NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA 1 NA NA NA 1 NA NA 1
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 188.99 189 189.85 189.95 189.99 190 190.45 190.99 193 193.15 194 194.29
## 1 NA NA NA NA NA NA NA NA NA 1 NA NA
## 2 NA NA NA NA NA NA NA 1 NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA 1 NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA 1 1 NA 2 1 NA NA NA NA 1 NA
## 9 NA NA NA NA 1 NA 1 NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA 1 NA NA NA
## 11 NA NA NA 1 1 NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 1 1 NA NA NA 1 NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA 1 NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA NA 1 NA NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA 2 1 NA NA NA NA NA 1
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA 1 NA NA NA NA NA NA NA
## 27 NA NA NA NA NA 1 NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 194.85 194.95 195 196 196.79 197.97 198 198.98 199 199.69 199.97 199.99
## 1 NA NA NA NA 1 NA NA NA 1 NA NA 2
## 2 NA NA 1 NA NA NA NA NA 2 NA 1 NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA 1 NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA 1
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 1 1 NA NA NA NA NA 1 1 NA NA 5
## 9 NA NA NA NA NA NA NA NA NA NA NA 3
## 10 NA NA 1 NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA 2
## 13 NA NA 2 NA NA NA NA NA 4 1 NA 7
## 14 NA NA NA 1 NA NA NA NA NA NA NA 2
## 15 NA NA NA NA NA NA NA NA 1 NA NA 1
## 16 NA NA NA NA NA NA NA NA NA NA NA 1
## 17 NA NA NA NA NA NA NA NA 1 NA NA 3
## 18 NA NA NA NA NA NA NA NA NA NA NA 1
## 19 NA NA NA NA NA NA NA NA NA NA NA 2
## 20 NA NA NA NA NA NA NA NA 1 NA NA 1
## 21 NA NA NA NA NA 1 NA NA 2 NA NA 1
## 22 NA NA 1 NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA 1 NA NA NA
## 24 NA NA 1 NA NA NA NA NA 2 NA NA 4
## 25 NA NA NA NA NA NA NA NA 1 NA 1 3
## 26 NA NA NA NA NA NA NA NA 1 NA NA 1
## 27 NA NA NA NA NA NA NA NA 2 NA NA NA
## 28 NA NA NA NA NA NA 1 NA NA NA NA 1
## 200 200.29 201.99 204 204.95 205 208 208.99 209 209.85 209.9 209.98
## 1 2 NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 1 NA NA NA NA NA NA NA NA NA NA NA
## 4 1 NA NA NA NA NA NA NA NA NA 1 NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 4 NA NA NA NA NA NA NA 1 NA NA NA
## 9 2 NA NA NA NA NA NA NA NA NA NA NA
## 10 1 NA NA 2 NA NA NA NA NA NA NA NA
## 11 NA NA NA NA 1 NA NA NA NA NA NA NA
## 12 1 NA NA NA NA NA NA NA NA NA NA NA
## 13 11 1 NA NA NA NA NA NA 2 NA NA NA
## 14 NA NA NA 1 NA NA NA NA NA NA NA NA
## 15 2 NA NA NA NA NA NA NA NA NA NA NA
## 16 3 NA NA NA NA NA NA NA NA NA NA NA
## 17 7 NA NA NA NA NA NA NA 2 NA NA NA
## 18 1 NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 3 NA NA NA NA NA NA NA NA NA NA NA
## 22 NA NA 1 NA NA NA NA NA NA NA NA 1
## 23 2 NA NA NA NA NA NA NA NA NA NA NA
## 24 6 NA NA NA NA 1 1 NA NA NA NA NA
## 25 NA NA NA NA NA NA NA 1 1 1 NA NA
## 26 2 NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 209.99 210 210.99 211.5 211.95 212.99 214.95 214.98 214.99 215 215.99
## 1 NA 1 NA NA NA NA 1 NA NA 1 NA
## 2 NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA 1 NA NA NA 1 1
## 9 NA NA NA NA 1 NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA
## 12 NA 1 NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA 1 NA
## 14 NA NA NA NA NA NA NA NA 1 2 NA
## 15 2 NA 1 NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA NA NA NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA
## 21 NA 1 NA NA NA NA NA NA NA 1 NA
## 22 NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA
## 24 NA 2 NA 1 NA NA NA 1 NA 2 NA
## 25 NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA 1
## 27 NA 1 NA NA NA NA NA NA NA NA NA
## 28 1 NA NA NA NA 1 NA NA NA NA NA
## 217 218 219 219.85 219.95 219.99 220 222.72 223 224 224.98 224.99 225
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA 1 NA NA NA
## 3 NA NA NA NA NA NA 1 NA NA NA NA NA 1
## 4 NA NA NA NA NA NA 1 NA NA NA NA NA 1
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA 1 NA NA NA NA NA NA NA NA 1
## 9 NA NA NA NA NA NA 1 NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA 1 NA NA NA NA NA NA
## 13 NA 1 NA NA NA 1 3 NA NA NA NA 1 5
## 14 NA NA NA NA NA 1 NA NA NA NA 1 NA 2
## 15 NA NA NA NA 1 2 NA NA NA NA NA NA 1
## 16 NA NA NA NA NA 1 3 NA NA NA NA NA NA
## 17 NA NA NA NA NA NA NA NA NA NA NA NA 1
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA 1 NA NA NA 4
## 22 NA NA NA NA NA NA NA 1 NA NA NA NA NA
## 23 1 NA NA NA NA NA NA NA NA NA NA NA 2
## 24 NA NA NA NA NA 2 NA NA NA NA NA NA 2
## 25 NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA 1 NA NA NA NA NA NA
## 227 227.88 227.95 228.59 228.88 229 229.95 229.97 229.98 229.99 230
## 1 NA NA NA NA NA NA NA NA NA NA 1
## 2 NA NA NA NA NA 1 NA NA NA 1 NA
## 3 NA NA NA NA NA NA 1 NA NA NA NA
## 4 NA NA NA NA NA 1 NA 1 NA 1 NA
## 5 NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA
## 7 1 NA NA NA NA NA NA NA NA NA NA
## 8 NA NA 1 1 NA NA NA NA NA 1 NA
## 9 NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA 1 NA NA NA 2 NA
## 14 NA NA NA NA 1 1 NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA 1 NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA NA NA 3 NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA 1 NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA 1 NA NA NA NA NA
## 21 NA NA NA NA NA 1 NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA 2
## 24 NA 1 NA NA NA NA NA NA NA NA 2
## 25 NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA
## 232.99 234 234.99 235 235.99 237 237.99 238 238.8 239 239.88 239.95
## 1 1 NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA 1 NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA 1 1 NA NA NA NA NA 1 NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA 1 NA NA 1 NA NA NA NA NA
## 14 NA NA NA NA NA NA NA 1 NA NA NA NA
## 15 NA 1 NA NA 1 NA NA NA NA 1 2 NA
## 16 NA NA NA NA NA NA NA NA NA NA NA 1
## 17 NA NA NA NA NA NA NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA 3 NA 1 NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA 1 NA NA
## 23 NA NA NA NA NA NA NA NA 1 NA NA NA
## 24 NA NA NA 1 NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA 1 NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 239.99 240 241.88 242 244.95 244.96 244.97 245 245.19 246 248 248.18
## 1 1 NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA 1 NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 1 NA NA NA NA NA NA NA NA NA NA NA
## 13 1 2 NA NA 1 NA NA NA 1 NA 1 NA
## 14 1 NA NA NA NA 1 NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA 1 NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA 1 NA 1 NA NA NA 2 NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 1 NA NA NA NA NA NA NA NA NA NA 1
## 22 NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA 1 NA NA NA NA
## 24 NA 1 1 NA NA NA 1 NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 1 NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 1 NA NA NA NA NA NA NA NA NA NA NA
## 249 249.59 249.95 249.97 249.98 249.99 250 252.88 252.99 254.99 255
## 1 2 NA NA NA NA 1 2 NA NA NA NA
## 2 1 NA NA NA NA 1 2 NA NA NA NA
## 3 NA NA NA NA NA 1 NA NA NA NA 1
## 4 NA NA NA NA NA NA 2 NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA 1 2 NA NA 3 NA NA NA NA
## 9 NA NA NA NA NA NA 1 NA NA NA NA
## 10 NA NA NA 1 NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA 1
## 13 2 NA NA 1 NA 8 7 NA 1 1 2
## 14 NA NA NA NA NA NA 3 NA NA NA NA
## 15 NA NA 1 NA NA 2 1 NA NA NA NA
## 16 NA 1 NA NA NA 2 4 NA NA NA NA
## 17 1 NA NA NA NA 1 5 NA NA NA NA
## 18 NA NA NA NA 1 NA 1 NA NA NA 1
## 19 NA NA NA NA 1 NA 3 NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA
## 21 1 NA NA NA NA NA 4 NA NA NA NA
## 22 1 NA NA NA NA NA 2 NA NA NA NA
## 23 1 NA NA NA NA NA NA NA NA NA NA
## 24 1 NA 1 NA NA 1 3 1 NA NA 1
## 25 1 NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA 1 2 NA NA NA NA
## 27 NA NA NA NA NA 1 NA NA NA NA NA
## 28 NA NA NA NA NA 1 NA NA NA NA NA
## 256.24 257.83 258.88 258.98 259 259.95 259.99 260 261.99 263.99 264.95
## 1 NA NA NA NA NA NA 1 1 NA NA NA
## 2 NA NA NA NA 1 NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA 1 NA NA 1 1 1 NA
## 14 NA NA NA NA 1 NA NA NA NA NA NA
## 15 NA NA NA NA NA NA 1 NA NA NA 1
## 16 NA NA NA NA NA NA 2 NA NA NA NA
## 17 NA NA NA NA NA NA 1 1 NA NA NA
## 18 1 NA NA NA NA NA NA NA NA NA NA
## 19 NA 1 NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA 1 NA NA NA
## 21 NA NA NA NA NA NA NA 1 NA NA NA
## 22 NA NA NA NA NA 1 NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA 1 2 1 NA 1 1 NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA 2 NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA
## 264.99 265 265.99 266.05 269 269.85 269.94 269.95 269.99 270 270.99 271
## 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA 1 NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA 1 NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA 1 NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA NA
## 14 NA 1 NA NA NA NA NA 1 2 1 NA NA
## 15 NA 1 NA NA NA NA NA NA NA NA NA NA
## 16 1 NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA 1 NA 1 NA NA NA NA NA NA
## 18 NA 1 NA NA NA NA NA NA NA NA 1 NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA 1 NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA 2 NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA
## 23 1 NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA 1
## 25 NA 1 NA NA NA NA NA NA NA NA NA NA
## 26 NA NA 1 NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 274 274.99 275 276.99 279 279.5 279.95 279.99 280 280.99 284 284.99 285
## 1 NA NA 1 NA 1 NA NA NA 1 NA NA NA NA
## 2 NA NA NA 1 NA NA NA NA NA NA NA NA 1
## 3 NA NA NA NA NA NA NA NA 1 NA NA NA NA
## 4 NA NA 1 NA NA NA 2 NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA 2 NA 1 NA NA 1 1 NA NA NA NA
## 9 NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA 5 NA NA 1 NA 2 3 1 NA NA NA
## 14 NA NA NA 1 NA NA NA 4 NA NA 1 NA NA
## 15 NA NA 1 NA NA NA NA NA NA NA NA NA 2
## 16 NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 17 1 NA 1 NA 1 NA NA 2 1 NA NA NA NA
## 18 NA NA NA NA NA NA NA 1 1 NA NA NA NA
## 19 NA 1 NA NA 1 NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA 1 NA NA NA NA NA
## 21 NA NA 1 NA NA NA NA NA 1 NA NA 2 3
## 22 NA NA NA NA NA NA NA 1 1 NA NA NA NA
## 23 NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 24 NA NA 4 NA 1 1 NA 1 2 NA NA NA 2
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 288 289 289.79 289.95 289.98 289.99 290 291.99 292.5 294.99 295 295.95
## 1 NA NA NA NA NA NA NA NA NA NA 1 NA
## 2 NA 1 NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA 1 NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA 1 NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 1 NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA 1 NA NA NA NA NA NA
## 13 NA 1 NA NA NA 1 1 1 NA NA 2 NA
## 14 NA NA NA NA NA NA NA NA NA NA 1 NA
## 15 NA NA NA NA 1 NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 1 NA 1 NA NA NA 2 NA NA 1 NA NA
## 18 NA NA NA NA NA NA NA NA NA NA 1 NA
## 19 NA NA NA NA NA NA 1 NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA 1 NA NA NA
## 21 NA 2 NA 1 NA NA 1 NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA 1 1
## 24 NA NA NA NA NA 1 NA NA NA NA 1 NA
## 25 NA 1 NA NA NA NA 1 NA NA NA NA NA
## 26 NA NA NA NA NA NA 1 NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA 1 NA NA NA NA NA
## 298 298.97 299 299.95 299.98 299.99 300 303.67 303.99 304.89 305 308
## 1 NA NA NA NA NA 1 5 NA NA NA NA NA
## 2 NA NA NA NA NA 1 3 NA NA NA NA NA
## 3 NA NA NA NA NA 1 NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA 1 NA NA NA NA 2 NA NA NA NA NA
## 9 NA NA NA NA NA NA 1 NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA 2 1 NA 2 4 NA 1 1 2 NA
## 14 1 NA 4 NA NA NA 2 NA NA NA NA NA
## 15 NA NA NA NA NA NA 1 NA NA NA NA NA
## 16 NA NA NA NA NA 1 2 NA NA NA NA NA
## 17 NA NA 3 NA NA 3 10 NA NA NA 1 NA
## 18 NA NA NA NA NA NA 1 NA NA NA NA NA
## 19 NA NA NA NA 1 1 NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA 3 NA NA 2 6 NA NA NA NA 1
## 22 NA NA 1 NA NA 1 1 1 NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 1 NA NA NA NA 2 3 NA NA NA NA NA
## 25 NA NA NA NA NA NA 1 NA NA NA NA NA
## 26 NA NA NA 1 NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 309.95 309.98 309.99 310 314.99 315 318 319 319.85 319.95 319.98 319.99
## 1 NA NA NA NA NA NA NA 2 NA NA NA NA
## 2 NA 1 NA NA NA NA NA 1 NA NA NA 2
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA 1 NA 1 NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA 1 NA 1 2 NA NA NA NA NA 1
## 14 NA NA 1 2 NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA 1 NA NA NA NA 1 NA NA
## 17 NA NA NA 1 NA NA NA 2 1 1 NA NA
## 18 NA NA NA 1 NA NA NA NA NA 1 NA 1
## 19 NA NA NA NA NA NA 1 NA NA NA NA 2
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 1 NA NA 1 NA 2 NA NA NA NA 1 1
## 22 NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA 1 NA 1 NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 320 320.99 322.99 324.9 324.99 325 327.58 329 329.99 330 331.99 332.5
## 1 NA NA NA NA NA 1 NA NA 1 NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA 1 NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 3 NA NA NA 2 1 NA 1 NA NA NA NA
## 14 NA NA NA 1 NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA 1 NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 1 2 NA NA 1 2 NA NA 3 NA NA NA
## 18 2 NA 1 NA NA 1 NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 1 NA NA NA NA 1 NA NA 1 NA NA NA
## 21 NA NA NA NA NA 5 1 3 5 NA 1 1
## 22 NA NA NA NA NA 1 NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 1 NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 334 334.95 334.99 335 339 339.5 339.98 339.99 340 344 344.95 345 346
## 1 NA NA 1 NA 1 NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA 1 NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA 1 NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA 2 NA NA NA NA 2 NA 1 NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA 1 NA NA NA NA
## 17 NA NA NA NA 1 NA NA 1 NA NA 1 NA 1
## 18 NA NA 1 NA 2 NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 1 NA NA 1 2 1 1 1 1 NA NA 2 NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA 1 NA NA NA NA NA 1 NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 347 347.24 348.6 349 349.95 349.99 350 350.25 351 358.24 358.87 359
## 1 1 NA NA NA NA 1 3 NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA 1 1 NA NA NA NA NA
## 9 NA NA NA NA NA NA 1 NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA 2 NA NA NA NA NA
## 14 NA NA NA NA NA NA 1 NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA 1 NA NA NA NA NA NA
## 17 NA NA NA 2 NA 3 7 NA NA NA NA NA
## 18 NA 1 NA NA 1 1 NA NA NA NA NA NA
## 19 NA NA NA 1 NA NA 1 NA NA 1 NA NA
## 20 NA NA NA NA NA NA 1 NA NA NA NA NA
## 21 NA NA NA 2 1 3 8 NA NA NA NA 1
## 22 NA NA NA NA NA NA 3 1 NA NA NA NA
## 23 NA NA NA NA NA NA 1 NA NA NA NA NA
## 24 NA NA 1 NA NA 1 5 NA 1 NA 1 NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 359.99 360 360.24 367.97 369.99 370 374.95 374.99 375 375.99 376 379
## 1 NA NA NA NA NA NA NA NA 2 NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA 1 NA NA NA NA 1 NA NA NA
## 15 NA NA NA 1 NA NA NA NA 1 NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 1 NA NA NA 1 1 2 NA 3 NA NA 2
## 18 NA 1 NA NA 1 NA NA NA NA 1 NA NA
## 19 NA NA 1 NA NA NA NA 1 NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA 1
## 21 1 NA NA NA NA 1 NA NA 4 NA NA NA
## 22 NA NA NA NA NA NA NA NA 1 NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA 1 NA
## 24 NA NA NA NA NA 1 NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA 1 NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 379.95 379.99 380 384.99 385 387 387.45 388.3 388.99 389 389.99 393
## 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA 1 NA NA NA NA NA NA 1 NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA 1
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA 1 NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA 3 2 1 NA NA NA NA 1 1 4 NA
## 18 NA 1 NA NA NA NA NA NA NA NA 2 NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 1 1 2 NA 1 1 NA NA NA NA 1 NA
## 22 NA NA NA NA NA NA NA NA NA NA 1 NA
## 23 NA 1 NA NA 1 NA NA NA NA NA NA NA
## 24 NA 1 NA NA 1 NA NA 1 NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA 1 NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 394.99 395 396 397.75 398.99 399 399.94 399.95 399.99 400 404.99 406
## 1 NA 1 NA NA NA 2 NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA 1 1 NA NA NA NA NA NA NA NA 1
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA 1 NA NA NA NA NA NA 1 3 NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA 1 NA NA
## 16 NA NA NA NA NA NA NA NA NA 1 NA NA
## 17 1 1 NA NA NA 3 NA 1 2 8 NA NA
## 18 NA NA NA NA NA 1 1 NA 3 2 NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA 2 NA NA 1 1 NA 1 NA
## 21 NA 1 NA NA NA 1 NA NA 5 4 NA NA
## 22 1 NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA 1 NA NA
## 24 NA NA NA 1 1 NA NA NA 1 2 NA NA
## 25 NA NA NA NA NA NA NA NA NA 1 NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 408 408.6 409.99 410 415 417 419 419.95 419.99 420 424.55 424.65 424.95
## 1 NA 1 NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA 1 NA NA NA 1 NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA 1 NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA 1 NA NA NA NA
## 14 NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 1 NA NA 1 3 NA 2 NA NA 1 1 1 1
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA 1 NA NA NA
## 22 NA NA NA NA NA NA NA 1 NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA 1 NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 424.99 425 425.99 426.3 426.99 429 429.95 429.99 430 438 438.99 439
## 1 NA NA 1 1 NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA 1 NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA 1 NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA 3 NA NA NA 1 1 1 2 1 1 NA
## 18 1 NA NA NA NA NA NA NA NA NA NA 1
## 19 NA NA NA NA NA NA NA 1 NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA 1 NA NA NA NA NA 1 NA NA NA NA
## 22 NA NA NA NA 1 NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA 1 NA NA NA
## 24 NA NA NA NA NA NA NA 1 NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA 1 NA NA NA NA NA NA
## 439.98 439.99 440 443.09 444.99 445 445.95 449 449.95 449.99 450 454
## 1 NA NA NA NA NA NA NA NA NA NA 2 NA
## 2 1 NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA 2 NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA 1 NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 1 4 1 1 1 1 NA 1 NA 2 8 1
## 18 NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA 1 NA NA
## 20 NA NA NA NA NA NA NA 1 NA NA NA NA
## 21 NA 1 NA NA NA NA 1 1 NA 2 2 NA
## 22 NA NA NA NA NA NA NA NA NA 1 NA NA
## 23 NA NA NA NA NA NA NA NA 1 NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 454.68 455 458 459 459.95 459.99 460 462.89 463.26 465.99 469 469.99
## 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA 1 NA NA NA
## 17 1 1 NA 1 1 1 NA 1 NA NA NA 1
## 18 NA NA NA NA NA NA NA NA NA NA NA 1
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA 2 NA NA
## 21 NA NA 1 NA NA 1 1 NA NA NA 1 NA
## 22 NA NA NA NA NA NA 1 NA NA NA NA 1
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 470 473.6 475 479.99 480 485 489.99 490 490.95 494.5 495.49 495.99
## 1 1 1 NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA 1 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA 1 NA NA NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA 1 NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA 1 NA 1 1 1 1 NA NA 1 1
## 18 NA NA NA NA 1 NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA 1 NA 1 NA NA NA 1 NA NA NA
## 21 NA NA 1 NA NA 1 NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA 1 NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 498.88 499 499.95 499.99 500 509 509.99 510 514.95 515 517.89 520 520.9
## 1 NA NA NA NA 2 NA NA NA NA NA NA NA 1
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA 1 NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA 1 NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA 1 NA NA NA NA NA NA 1 NA
## 17 NA 2 NA 4 7 NA 1 1 NA 1 1 1 NA
## 18 NA 1 NA 1 NA NA NA NA NA NA NA NA NA
## 19 NA 1 1 NA 1 1 NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA 1 NA NA NA NA
## 21 2 2 NA 4 3 1 NA 1 NA NA NA NA NA
## 22 NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 525 528 529 529.95 529.99 535 539.95 540 544.49 549 549.9 549.95 549.99
## 1 NA NA NA NA NA 1 NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 9 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA 1 1 NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 2 1 1 1 1 NA 1 NA NA 2 1 NA 4
## 18 1 NA NA NA NA NA NA NA NA NA NA 1 NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA 1
## 21 1 NA NA NA 1 NA NA NA NA NA NA NA 1
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 550 554.77 558.17 559 559.99 560 561.53 565.95 569 573.74 575 579.99
## 1 1 1 NA NA NA NA 1 NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA 1 NA NA NA NA 1 NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 6 NA NA 1 2 3 NA NA NA NA 2 2
## 18 1 NA 1 NA NA NA NA NA NA NA NA NA
## 19 2 NA NA NA NA NA NA NA NA NA NA NA
## 20 1 NA NA NA NA NA NA 1 NA NA NA 1
## 21 1 NA NA NA 1 NA NA NA 1 NA 2 1
## 22 1 NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 585.99 588.18 589 589.99 590 595 598.98 599 599.99 600 609.99 614.99
## 1 NA NA NA NA 1 1 NA 1 2 NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA 2 NA NA NA NA NA NA NA 1 NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 1 NA NA 1 1 1 1 NA 1 1 NA 1
## 18 NA NA 1 NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA 1 NA NA 4 NA 1 1
## 22 NA NA NA NA NA NA NA NA 1 NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 615.99 619 619.99 624.99 625 629 630 634.99 639 639.99 640 645 645.99
## 1 NA NA NA NA NA NA NA NA NA 1 1 NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 1 1 NA 1 1 1 1 1 2 2 NA 1 1
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA 1 NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 648 649.95 649.99 650 659.49 660 670 675 679.95 679.99 680 689.99 695
## 1 NA NA NA NA NA NA NA NA NA 1 NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA 2 1 NA NA NA NA NA NA NA NA 1
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 2 1 1 3 NA 1 1 1 1 1 1 NA NA
## 18 NA NA NA NA 1 NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA 1 NA NA NA NA NA NA NA 1 NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 699 699.95 700 710 720.12 729.99 730 740 749 749.95 749.99 750 785 789
## 1 NA NA 2 NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA 1 NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 14 NA 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 1 NA NA 1 NA 2 1 1 2 2 1 1 1 1
## 18 NA NA NA NA NA NA NA NA NA NA NA 1 NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA 3 NA NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 789.99 795 795.99 798 799 799.99 800 820 829.99 879.99 899.99 900 939
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 1 1 1 1 1 NA 1 1 1 1 1 1 1
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA 1 NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA 1 NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 948.98 999 999.99 .entropy .knt
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## 7 NA NA NA NA NA
## 8 NA NA NA NA NA
## 9 NA NA NA NA NA
## 10 NA NA NA NA NA
## 11 NA NA NA NA NA
## 12 NA NA NA NA NA
## 13 NA NA NA NA NA
## 14 NA NA NA NA NA
## 15 NA NA NA NA NA
## 16 NA NA NA NA NA
## 17 NA NA NA NA NA
## 18 NA NA NA NA NA
## 19 NA NA NA NA NA
## 20 NA NA NA NA NA
## 21 1 NA NA NA NA
## 22 NA NA NA NA NA
## 23 NA NA NA NA NA
## 24 NA 1 NA NA NA
## 25 NA NA NA NA NA
## 26 NA NA NA NA NA
## 27 NA NA 1 NA NA
## 28 NA NA NA NA NA# Last call for data modifications
#stop(here") # sav_allobs_df <- glb_allobs_df
# glb_allobs_df[(glb_allobs_df$PropR == 0.75) & (glb_allobs_df$State == "Hawaii"), "PropR.fctr"] <- "N"
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 manage.missing.data 4 1 89.749 94.619 4.87
## 8 select.features 5 0 94.619 NA NAStep 5.0: select features
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## startprice.log startprice.log 0.714953462
## biddable biddable -0.478968707
## prdline.my.fctr prdline.my.fctr 0.291582777
## condition.fctr condition.fctr 0.205950857
## color.fctr color.fctr 0.184828855
## D.ratio.sum.TfIdf.nwrds D.ratio.sum.TfIdf.nwrds -0.137639619
## D.TfIdf.sum.post.stop D.TfIdf.sum.post.stop -0.134930186
## D.ratio.nstopwrds.nwrds D.ratio.nstopwrds.nwrds 0.134172297
## D.TfIdf.sum.post.stem D.TfIdf.sum.post.stem -0.131266679
## D.sum.TfIdf D.sum.TfIdf -0.131266679
## D.npnct24.log D.npnct24.log -0.130780343
## D.TfIdf.sum.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio 0.129762736
## D.nuppr.log D.nuppr.log -0.117520055
## D.nchrs.log D.nchrs.log -0.116806788
## D.terms.n.post.stem.log D.terms.n.post.stem.log -0.111541664
## D.nwrds.unq.log D.nwrds.unq.log -0.111541664
## D.terms.n.post.stop.log D.terms.n.post.stop.log -0.111133658
## D.nwrds.log D.nwrds.log -0.102544451
## D.terms.n.post.stem D.terms.n.post.stem -0.093178030
## D.terms.n.post.stop D.terms.n.post.stop -0.092670495
## D.T.screen D.T.screen -0.088331760
## D.npnct13.log D.npnct13.log -0.083057559
## D.npnct11.log D.npnct11.log -0.080106895
## D.nstopwrds.log D.nstopwrds.log -0.071828346
## D.T.work D.T.work -0.070081986
## .clusterid .clusterid -0.060520531
## .clusterid.fctr .clusterid.fctr -0.060520531
## carrier.fctr carrier.fctr 0.057799434
## D.npnct03.log D.npnct03.log -0.054044001
## D.T.great D.T.great -0.053229243
## D.T.good D.T.good -0.052938979
## D.T.ipad D.T.ipad -0.050219619
## D.npnct06.log D.npnct06.log -0.048540915
## D.npnct12.log D.npnct12.log -0.047449133
## storage.fctr storage.fctr 0.037408089
## D.npnct16.log D.npnct16.log -0.035504765
## D.npnct10.log D.npnct10.log 0.034091652
## D.npnct07.log D.npnct07.log -0.032922510
## D.P.air D.P.air -0.025982300
## D.npnct01.log D.npnct01.log -0.024992771
## D.npnct14.log D.npnct14.log 0.023604586
## D.npnct15.log D.npnct15.log -0.023187752
## D.P.mini D.P.mini -0.017493744
## D.npnct05.log D.npnct05.log -0.015097464
## D.T.new D.T.new 0.014424686
## D.npnct08.log D.npnct08.log -0.014183822
## D.T.excel D.T.excel -0.011551038
## UniqueID UniqueID -0.009667837
## idseq.my idseq.my -0.009667837
## .rnorm .rnorm -0.008500798
## D.ndgts.log D.ndgts.log -0.007780470
## D.T.scratch D.T.scratch 0.007525897
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio 0.005457044
## D.T.use D.T.use 0.005272991
## cellular.fctr cellular.fctr 0.003201285
## D.T.condit D.T.condit -0.001531310
## sold sold NA
## D.npnct02.log D.npnct02.log NA
## D.npnct04.log D.npnct04.log NA
## D.npnct09.log D.npnct09.log NA
## D.npnct17.log D.npnct17.log NA
## D.npnct18.log D.npnct18.log NA
## D.npnct19.log D.npnct19.log NA
## D.npnct20.log D.npnct20.log NA
## D.npnct21.log D.npnct21.log NA
## D.npnct22.log D.npnct22.log NA
## D.npnct23.log D.npnct23.log NA
## D.npnct25.log D.npnct25.log NA
## D.npnct26.log D.npnct26.log NA
## D.npnct27.log D.npnct27.log NA
## D.npnct28.log D.npnct28.log NA
## D.npnct29.log D.npnct29.log NA
## D.npnct30.log D.npnct30.log NA
## D.P.http D.P.http NA
## exclude.as.feat cor.y.abs
## startprice.log 1 0.714953462
## biddable 0 0.478968707
## prdline.my.fctr 0 0.291582777
## condition.fctr 0 0.205950857
## color.fctr 0 0.184828855
## D.ratio.sum.TfIdf.nwrds 0 0.137639619
## D.TfIdf.sum.post.stop 0 0.134930186
## D.ratio.nstopwrds.nwrds 0 0.134172297
## D.TfIdf.sum.post.stem 0 0.131266679
## D.sum.TfIdf 0 0.131266679
## D.npnct24.log 0 0.130780343
## D.TfIdf.sum.stem.stop.Ratio 0 0.129762736
## D.nuppr.log 0 0.117520055
## D.nchrs.log 0 0.116806788
## D.terms.n.post.stem.log 0 0.111541664
## D.nwrds.unq.log 0 0.111541664
## D.terms.n.post.stop.log 0 0.111133658
## D.nwrds.log 0 0.102544451
## D.terms.n.post.stem 0 0.093178030
## D.terms.n.post.stop 0 0.092670495
## D.T.screen 1 0.088331760
## D.npnct13.log 0 0.083057559
## D.npnct11.log 0 0.080106895
## D.nstopwrds.log 0 0.071828346
## D.T.work 1 0.070081986
## .clusterid 1 0.060520531
## .clusterid.fctr 0 0.060520531
## carrier.fctr 0 0.057799434
## D.npnct03.log 0 0.054044001
## D.T.great 1 0.053229243
## D.T.good 1 0.052938979
## D.T.ipad 1 0.050219619
## D.npnct06.log 0 0.048540915
## D.npnct12.log 0 0.047449133
## storage.fctr 0 0.037408089
## D.npnct16.log 0 0.035504765
## D.npnct10.log 0 0.034091652
## D.npnct07.log 0 0.032922510
## D.P.air 1 0.025982300
## D.npnct01.log 0 0.024992771
## D.npnct14.log 0 0.023604586
## D.npnct15.log 0 0.023187752
## D.P.mini 1 0.017493744
## D.npnct05.log 0 0.015097464
## D.T.new 1 0.014424686
## D.npnct08.log 0 0.014183822
## D.T.excel 1 0.011551038
## UniqueID 1 0.009667837
## idseq.my 0 0.009667837
## .rnorm 0 0.008500798
## D.ndgts.log 0 0.007780470
## D.T.scratch 1 0.007525897
## D.terms.n.stem.stop.Ratio 0 0.005457044
## D.T.use 1 0.005272991
## cellular.fctr 0 0.003201285
## D.T.condit 1 0.001531310
## sold 1 NA
## D.npnct02.log 0 NA
## D.npnct04.log 0 NA
## D.npnct09.log 0 NA
## D.npnct17.log 0 NA
## D.npnct18.log 0 NA
## D.npnct19.log 0 NA
## D.npnct20.log 0 NA
## D.npnct21.log 0 NA
## D.npnct22.log 0 NA
## D.npnct23.log 0 NA
## D.npnct25.log 0 NA
## D.npnct26.log 0 NA
## D.npnct27.log 0 NA
## D.npnct28.log 0 NA
## D.npnct29.log 0 NA
## D.npnct30.log 0 NA
## D.P.http 1 NA# sav_feats_df <- glb_feats_df; glb_feats_df <- sav_feats_df
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df,
rsp_var=glb_rsp_var)))## [1] "cor(D.TfIdf.sum.post.stem, D.sum.TfIdf)=1.0000"
## [1] "cor(startprice, D.TfIdf.sum.post.stem)=-0.1313"
## [1] "cor(startprice, D.sum.TfIdf)=-0.1313"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.sum.TfIdf as highly correlated with
## D.TfIdf.sum.post.stem## [1] "cor(D.nwrds.unq.log, D.terms.n.post.stem.log)=1.0000"
## [1] "cor(startprice, D.nwrds.unq.log)=-0.1115"
## [1] "cor(startprice, D.terms.n.post.stem.log)=-0.1115"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stem.log as highly correlated
## with D.nwrds.unq.log## [1] "cor(D.nwrds.unq.log, D.terms.n.post.stop.log)=0.9998"
## [1] "cor(startprice, D.nwrds.unq.log)=-0.1115"
## [1] "cor(startprice, D.terms.n.post.stop.log)=-0.1111"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stop.log as highly correlated
## with D.nwrds.unq.log## [1] "cor(D.nchrs.log, D.nuppr.log)=0.9998"
## [1] "cor(startprice, D.nchrs.log)=-0.1168"
## [1] "cor(startprice, D.nuppr.log)=-0.1175"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nchrs.log as highly correlated with
## D.nuppr.log## [1] "cor(D.terms.n.post.stem, D.terms.n.post.stop)=0.9991"
## [1] "cor(startprice, D.terms.n.post.stem)=-0.0932"
## [1] "cor(startprice, D.terms.n.post.stop)=-0.0927"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stop as highly correlated with
## D.terms.n.post.stem## [1] "cor(D.TfIdf.sum.post.stem, D.TfIdf.sum.post.stop)=0.9981"
## [1] "cor(startprice, D.TfIdf.sum.post.stem)=-0.1313"
## [1] "cor(startprice, D.TfIdf.sum.post.stop)=-0.1349"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.TfIdf.sum.post.stem as highly correlated with
## D.TfIdf.sum.post.stop## [1] "cor(D.nuppr.log, D.nwrds.unq.log)=0.9930"
## [1] "cor(startprice, D.nuppr.log)=-0.1175"
## [1] "cor(startprice, D.nwrds.unq.log)=-0.1115"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.unq.log as highly correlated with
## D.nuppr.log## [1] "cor(D.nuppr.log, D.nwrds.log)=0.9910"
## [1] "cor(startprice, D.nuppr.log)=-0.1175"
## [1] "cor(startprice, D.nwrds.log)=-0.1025"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.log as highly correlated with
## D.nuppr.log## [1] "cor(D.npnct24.log, D.nuppr.log)=0.9792"
## [1] "cor(startprice, D.npnct24.log)=-0.1308"
## [1] "cor(startprice, D.nuppr.log)=-0.1175"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nuppr.log as highly correlated with
## D.npnct24.log## [1] "cor(D.npnct24.log, D.ratio.nstopwrds.nwrds)=-0.9680"
## [1] "cor(startprice, D.npnct24.log)=-0.1308"
## [1] "cor(startprice, D.ratio.nstopwrds.nwrds)=0.1342"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct24.log as highly correlated with
## D.ratio.nstopwrds.nwrds## [1] "cor(D.npnct06.log, D.npnct16.log)=0.9445"
## [1] "cor(startprice, D.npnct06.log)=-0.0485"
## [1] "cor(startprice, D.npnct16.log)=-0.0355"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct16.log as highly correlated with
## D.npnct06.log## [1] "cor(D.TfIdf.sum.post.stop, D.ratio.nstopwrds.nwrds)=-0.9252"
## [1] "cor(startprice, D.TfIdf.sum.post.stop)=-0.1349"
## [1] "cor(startprice, D.ratio.nstopwrds.nwrds)=0.1342"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.ratio.nstopwrds.nwrds as highly correlated
## with D.TfIdf.sum.post.stop## [1] "cor(D.nstopwrds.log, D.terms.n.post.stem)=0.9106"
## [1] "cor(startprice, D.nstopwrds.log)=-0.0718"
## [1] "cor(startprice, D.terms.n.post.stem)=-0.0932"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nstopwrds.log as highly correlated with
## D.terms.n.post.stem## [1] "cor(D.TfIdf.sum.post.stop, D.terms.n.post.stem)=0.8881"
## [1] "cor(startprice, D.TfIdf.sum.post.stop)=-0.1349"
## [1] "cor(startprice, D.terms.n.post.stem)=-0.0932"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stem as highly correlated with
## D.TfIdf.sum.post.stop## [1] "cor(D.npnct03.log, D.npnct06.log)=0.7921"
## [1] "cor(startprice, D.npnct03.log)=-0.0540"
## [1] "cor(startprice, D.npnct06.log)=-0.0485"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct06.log as highly correlated with
## D.npnct03.log## id cor.y exclude.as.feat cor.y.abs
## 73 startprice.log 0.714953462 1 0.714953462
## 71 prdline.my.fctr 0.291582777 0 0.291582777
## 69 condition.fctr 0.205950857 0 0.205950857
## 68 color.fctr 0.184828855 0 0.184828855
## 56 D.ratio.nstopwrds.nwrds 0.134172297 0 0.134172297
## 19 D.TfIdf.sum.stem.stop.Ratio 0.129762736 0 0.129762736
## 66 carrier.fctr 0.057799434 0 0.057799434
## 74 storage.fctr 0.037408089 0 0.037408089
## 31 D.npnct10.log 0.034091652 0 0.034091652
## 35 D.npnct14.log 0.023604586 0 0.023604586
## 12 D.T.new 0.014424686 1 0.014424686
## 13 D.T.scratch 0.007525897 1 0.007525897
## 63 D.terms.n.stem.stop.Ratio 0.005457044 0 0.005457044
## 15 D.T.use 0.005272991 1 0.005272991
## 67 cellular.fctr 0.003201285 0 0.003201285
## 7 D.T.condit -0.001531310 1 0.001531310
## 21 D.ndgts.log -0.007780470 0 0.007780470
## 3 .rnorm -0.008500798 0 0.008500798
## 64 UniqueID -0.009667837 1 0.009667837
## 70 idseq.my -0.009667837 0 0.009667837
## 8 D.T.excel -0.011551038 1 0.011551038
## 29 D.npnct08.log -0.014183822 0 0.014183822
## 26 D.npnct05.log -0.015097464 0 0.015097464
## 6 D.P.mini -0.017493744 1 0.017493744
## 36 D.npnct15.log -0.023187752 0 0.023187752
## 22 D.npnct01.log -0.024992771 0 0.024992771
## 4 D.P.air -0.025982300 1 0.025982300
## 28 D.npnct07.log -0.032922510 0 0.032922510
## 37 D.npnct16.log -0.035504765 0 0.035504765
## 33 D.npnct12.log -0.047449133 0 0.047449133
## 27 D.npnct06.log -0.048540915 0 0.048540915
## 11 D.T.ipad -0.050219619 1 0.050219619
## 9 D.T.good -0.052938979 1 0.052938979
## 10 D.T.great -0.053229243 1 0.053229243
## 24 D.npnct03.log -0.054044001 0 0.054044001
## 1 .clusterid -0.060520531 1 0.060520531
## 2 .clusterid.fctr -0.060520531 0 0.060520531
## 16 D.T.work -0.070081986 1 0.070081986
## 52 D.nstopwrds.log -0.071828346 0 0.071828346
## 32 D.npnct11.log -0.080106895 0 0.080106895
## 34 D.npnct13.log -0.083057559 0 0.083057559
## 14 D.T.screen -0.088331760 1 0.088331760
## 61 D.terms.n.post.stop -0.092670495 0 0.092670495
## 59 D.terms.n.post.stem -0.093178030 0 0.093178030
## 54 D.nwrds.log -0.102544451 0 0.102544451
## 62 D.terms.n.post.stop.log -0.111133658 0 0.111133658
## 55 D.nwrds.unq.log -0.111541664 0 0.111541664
## 60 D.terms.n.post.stem.log -0.111541664 0 0.111541664
## 20 D.nchrs.log -0.116806788 0 0.116806788
## 53 D.nuppr.log -0.117520055 0 0.117520055
## 45 D.npnct24.log -0.130780343 0 0.130780343
## 17 D.TfIdf.sum.post.stem -0.131266679 0 0.131266679
## 58 D.sum.TfIdf -0.131266679 0 0.131266679
## 18 D.TfIdf.sum.post.stop -0.134930186 0 0.134930186
## 57 D.ratio.sum.TfIdf.nwrds -0.137639619 0 0.137639619
## 65 biddable -0.478968707 0 0.478968707
## 5 D.P.http NA 1 NA
## 23 D.npnct02.log NA 0 NA
## 25 D.npnct04.log NA 0 NA
## 30 D.npnct09.log NA 0 NA
## 38 D.npnct17.log NA 0 NA
## 39 D.npnct18.log NA 0 NA
## 40 D.npnct19.log NA 0 NA
## 41 D.npnct20.log NA 0 NA
## 42 D.npnct21.log NA 0 NA
## 43 D.npnct22.log NA 0 NA
## 44 D.npnct23.log NA 0 NA
## 46 D.npnct25.log NA 0 NA
## 47 D.npnct26.log NA 0 NA
## 48 D.npnct27.log NA 0 NA
## 49 D.npnct28.log NA 0 NA
## 50 D.npnct29.log NA 0 NA
## 51 D.npnct30.log NA 0 NA
## 72 sold NA 1 NA
## cor.high.X freqRatio percentUnique zeroVar nzv myNearZV
## 73 <NA> 4.000000 28.3720930 FALSE FALSE FALSE
## 71 <NA> 1.020408 0.8139535 FALSE FALSE FALSE
## 69 <NA> 5.528302 0.6976744 FALSE FALSE FALSE
## 68 <NA> 1.513636 0.5813953 FALSE FALSE FALSE
## 56 D.TfIdf.sum.post.stop 13.578947 7.4418605 FALSE FALSE FALSE
## 19 <NA> 106.000000 33.9534884 FALSE FALSE FALSE
## 66 <NA> 4.067164 0.8139535 FALSE FALSE FALSE
## 74 <NA> 2.936306 0.5813953 FALSE FALSE FALSE
## 31 <NA> 429.000000 0.2325581 FALSE TRUE TRUE
## 35 <NA> 65.076923 0.3488372 FALSE TRUE FALSE
## 12 <NA> 117.714286 1.5116279 FALSE TRUE FALSE
## 13 <NA> 48.125000 1.7441860 FALSE TRUE FALSE
## 63 <NA> 104.125000 0.9302326 FALSE TRUE FALSE
## 15 <NA> 51.400000 1.6279070 FALSE TRUE FALSE
## 67 <NA> 2.400881 0.3488372 FALSE FALSE FALSE
## 7 <NA> 35.047619 1.7441860 FALSE TRUE FALSE
## 21 <NA> 38.190476 1.2790698 FALSE TRUE FALSE
## 3 <NA> 1.000000 100.0000000 FALSE FALSE FALSE
## 64 <NA> 1.000000 100.0000000 FALSE FALSE FALSE
## 70 <NA> 1.000000 100.0000000 FALSE FALSE FALSE
## 8 <NA> 138.333333 1.3953488 FALSE TRUE FALSE
## 29 <NA> 70.416667 0.3488372 FALSE TRUE FALSE
## 26 <NA> 214.000000 0.2325581 FALSE TRUE FALSE
## 6 <NA> 121.714286 0.3488372 FALSE TRUE FALSE
## 36 <NA> 94.111111 0.3488372 FALSE TRUE FALSE
## 22 <NA> 48.941176 0.5813953 FALSE TRUE FALSE
## 4 <NA> 121.857143 0.2325581 FALSE TRUE FALSE
## 28 <NA> 859.000000 0.2325581 FALSE TRUE TRUE
## 37 D.npnct06.log 52.125000 0.3488372 FALSE TRUE FALSE
## 33 <NA> 29.571429 0.3488372 FALSE TRUE FALSE
## 27 D.npnct03.log 64.461538 0.3488372 FALSE TRUE FALSE
## 11 <NA> 46.647059 1.6279070 FALSE TRUE FALSE
## 9 <NA> 50.062500 1.7441860 FALSE TRUE FALSE
## 10 <NA> 117.571429 1.3953488 FALSE TRUE FALSE
## 24 <NA> 84.500000 0.3488372 FALSE TRUE FALSE
## 1 <NA> 4.617188 0.5813953 FALSE FALSE FALSE
## 2 <NA> 4.617188 0.5813953 FALSE FALSE FALSE
## 16 <NA> 89.444444 1.5116279 FALSE TRUE FALSE
## 52 D.terms.n.post.stem 14.075000 1.7441860 FALSE FALSE FALSE
## 32 <NA> 9.701299 0.8139535 FALSE FALSE FALSE
## 34 <NA> 5.935780 0.6976744 FALSE FALSE FALSE
## 14 <NA> 52.600000 1.6279070 FALSE TRUE FALSE
## 61 D.terms.n.post.stem 8.896552 1.6279070 FALSE FALSE FALSE
## 59 D.TfIdf.sum.post.stop 8.322581 1.6279070 FALSE FALSE FALSE
## 54 D.nuppr.log 16.125000 2.7906977 FALSE FALSE FALSE
## 62 D.nwrds.unq.log 8.896552 1.6279070 FALSE FALSE FALSE
## 55 D.nuppr.log 8.322581 1.6279070 FALSE FALSE FALSE
## 60 D.nwrds.unq.log 8.322581 1.6279070 FALSE FALSE FALSE
## 20 D.nuppr.log 19.846154 10.6976744 FALSE FALSE FALSE
## 53 D.npnct24.log 19.111111 8.6046512 FALSE TRUE FALSE
## 45 D.ratio.nstopwrds.nwrds 1.500000 0.2325581 FALSE FALSE FALSE
## 17 D.TfIdf.sum.post.stop 103.200000 35.2325581 FALSE FALSE FALSE
## 58 D.TfIdf.sum.post.stem 103.200000 35.2325581 FALSE FALSE FALSE
## 18 <NA> 103.200000 35.3488372 FALSE FALSE FALSE
## 57 <NA> 103.200000 35.5813953 FALSE FALSE FALSE
## 65 <NA> 2.909091 0.2325581 FALSE FALSE FALSE
## 5 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 23 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 25 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 30 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 38 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 39 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 40 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 41 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 42 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 43 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 44 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 46 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 47 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 48 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 49 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 50 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 51 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 72 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## is.cor.y.abs.low
## 73 FALSE
## 71 FALSE
## 69 FALSE
## 68 FALSE
## 56 FALSE
## 19 FALSE
## 66 FALSE
## 74 FALSE
## 31 FALSE
## 35 FALSE
## 12 FALSE
## 13 TRUE
## 63 TRUE
## 15 TRUE
## 67 TRUE
## 7 TRUE
## 21 TRUE
## 3 FALSE
## 64 FALSE
## 70 FALSE
## 8 FALSE
## 29 FALSE
## 26 FALSE
## 6 FALSE
## 36 FALSE
## 22 FALSE
## 4 FALSE
## 28 FALSE
## 37 FALSE
## 33 FALSE
## 27 FALSE
## 11 FALSE
## 9 FALSE
## 10 FALSE
## 24 FALSE
## 1 FALSE
## 2 FALSE
## 16 FALSE
## 52 FALSE
## 32 FALSE
## 34 FALSE
## 14 FALSE
## 61 FALSE
## 59 FALSE
## 54 FALSE
## 62 FALSE
## 55 FALSE
## 60 FALSE
## 20 FALSE
## 53 FALSE
## 45 FALSE
## 17 FALSE
## 58 FALSE
## 18 FALSE
## 57 FALSE
## 65 FALSE
## 5 NA
## 23 NA
## 25 NA
## 30 NA
## 38 NA
## 39 NA
## 40 NA
## 41 NA
## 42 NA
## 43 NA
## 44 NA
## 46 NA
## 47 NA
## 48 NA
## 49 NA
## 50 NA
## 51 NA
## 72 NA#subset(glb_feats_df, id %in% c("A.nuppr.log", "S.nuppr.log"))
print(myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
colorcol_name="myNearZV", jitter=TRUE) +
geom_point(aes(shape=nzv)) + xlim(-5, 25))## Warning in myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "myNearZV", : converting myNearZV to class:factor## Warning: Removed 9 rows containing missing values (geom_point).## Warning: Removed 9 rows containing missing values (geom_point).## Warning: Removed 9 rows containing missing values (geom_point).
print(subset(glb_feats_df, myNearZV))## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 31 D.npnct10.log 0.03409165 0 0.03409165 <NA>
## 28 D.npnct07.log -0.03292251 0 0.03292251 <NA>
## 5 D.P.http NA 1 NA <NA>
## 23 D.npnct02.log NA 0 NA <NA>
## 25 D.npnct04.log NA 0 NA <NA>
## 30 D.npnct09.log NA 0 NA <NA>
## 38 D.npnct17.log NA 0 NA <NA>
## 39 D.npnct18.log NA 0 NA <NA>
## 40 D.npnct19.log NA 0 NA <NA>
## 41 D.npnct20.log NA 0 NA <NA>
## 42 D.npnct21.log NA 0 NA <NA>
## 43 D.npnct22.log NA 0 NA <NA>
## 44 D.npnct23.log NA 0 NA <NA>
## 46 D.npnct25.log NA 0 NA <NA>
## 47 D.npnct26.log NA 0 NA <NA>
## 48 D.npnct27.log NA 0 NA <NA>
## 49 D.npnct28.log NA 0 NA <NA>
## 50 D.npnct29.log NA 0 NA <NA>
## 51 D.npnct30.log NA 0 NA <NA>
## 72 sold NA 1 NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 31 429 0.2325581 FALSE TRUE TRUE FALSE
## 28 859 0.2325581 FALSE TRUE TRUE FALSE
## 5 0 0.1162791 TRUE TRUE TRUE NA
## 23 0 0.1162791 TRUE TRUE TRUE NA
## 25 0 0.1162791 TRUE TRUE TRUE NA
## 30 0 0.1162791 TRUE TRUE TRUE NA
## 38 0 0.1162791 TRUE TRUE TRUE NA
## 39 0 0.1162791 TRUE TRUE TRUE NA
## 40 0 0.1162791 TRUE TRUE TRUE NA
## 41 0 0.1162791 TRUE TRUE TRUE NA
## 42 0 0.1162791 TRUE TRUE TRUE NA
## 43 0 0.1162791 TRUE TRUE TRUE NA
## 44 0 0.1162791 TRUE TRUE TRUE NA
## 46 0 0.1162791 TRUE TRUE TRUE NA
## 47 0 0.1162791 TRUE TRUE TRUE NA
## 48 0 0.1162791 TRUE TRUE TRUE NA
## 49 0 0.1162791 TRUE TRUE TRUE NA
## 50 0 0.1162791 TRUE TRUE TRUE NA
## 51 0 0.1162791 TRUE TRUE TRUE NA
## 72 0 0.1162791 TRUE TRUE TRUE NAglb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
subset(glb_feats_df, myNearZV)$id)]
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
if (!is.null(glb_interaction_only_features))
glb_feats_df[glb_feats_df$id %in% glb_interaction_only_features, "interaction.feat"] <-
names(glb_interaction_only_features) else
glb_feats_df$interaction.feat <- NA
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## named integer(0)
## [1] "numeric data w/ 0s in : "
## biddable startprice.log cellular.fctr
## 1444 31 1597
## D.terms.n.post.stop D.terms.n.post.stop.log D.TfIdf.sum.post.stop
## 1521 1521 1521
## D.terms.n.post.stem D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 1521 1521 1521
## D.T.condit D.T.use D.T.scratch
## 2161 2366 2371
## D.T.new D.T.good D.T.ipad
## 2501 2460 2425
## D.T.screen D.T.great D.T.work
## 2444 2532 2459
## D.T.excel D.nwrds.log D.nwrds.unq.log
## 2557 1520 1521
## D.sum.TfIdf D.ratio.sum.TfIdf.nwrds D.nchrs.log
## 1521 1521 1520
## D.nuppr.log D.ndgts.log D.npnct01.log
## 1522 2426 2579
## D.npnct03.log D.npnct05.log D.npnct06.log
## 2614 2592 2554
## D.npnct08.log D.npnct11.log D.npnct12.log
## 2581 2301 2537
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1932 2582 2637
## D.npnct16.log D.npnct24.log D.nstopwrds.log
## 2546 1520 1663
## D.P.mini D.P.air
## 2623 2637
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 select.features 5 0 94.619 97.984 3.365
## 9 partition.data.training 6 0 97.985 NA NAStep 6.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
set.seed(glb_split_sample.seed)
OOB_size <- nrow(glb_newobs_df) * 1.1
if (is.null(glb_category_var)) {
require(caTools)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=OOB_size / nrow(glb_trnobs_df))
glb_OOBobs_df <- glb_trnobs_df[split ,]
glb_fitobs_df <- glb_trnobs_df[!split, ]
} else {
sample_vars <- c(glb_rsp_var_raw, glb_category_var)
rspvar_freq_df <- orderBy(reformulate(glb_rsp_var_raw),
mycreate_sqlxtab_df(glb_trnobs_df, glb_rsp_var_raw))
OOB_rspvar_size <- 1.0 * OOB_size * rspvar_freq_df$.n / sum(rspvar_freq_df$.n)
newobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_newobs_df, glb_category_var))
trnobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_trnobs_df, glb_category_var))
allobs_freq_df <- merge(newobs_freq_df, trnobs_freq_df, by=glb_category_var,
all=TRUE, sort=TRUE, suffixes=c(".Tst", ".Train"))
allobs_freq_df[is.na(allobs_freq_df)] <- 0
OOB_strata_size <- ceiling(
as.vector(matrix(allobs_freq_df$.n.Tst * 1.0 / sum(allobs_freq_df$.n.Tst)) %*%
matrix(OOB_rspvar_size, nrow=1)))
OOB_strata_size[OOB_strata_size == 0] <- 1
OOB_strata_df <- expand.grid(glb_rsp_var_raw=rspvar_freq_df[, glb_rsp_var_raw],
glb_category_var=allobs_freq_df[, glb_category_var])
names(OOB_strata_df) <- sample_vars
OOB_strata_df <- orderBy(reformulate(sample_vars), OOB_strata_df)
trnobs_univ_df <- orderBy(reformulate(sample_vars),
mycreate_sqlxtab_df(glb_trnobs_df, sample_vars))
trnobs_univ_df <- merge(trnobs_univ_df, OOB_strata_df, all=TRUE)
tmp_trnobs_df <- orderBy(reformulate(c(glb_rsp_var_raw, glb_category_var)),
glb_trnobs_df)
require(sampling)
split_strata <- strata(tmp_trnobs_df,
stratanames=c(glb_rsp_var_raw, glb_category_var),
size=OOB_strata_size[!is.na(trnobs_univ_df$.n)],
method="srswor")
glb_OOBobs_df <- getdata(tmp_trnobs_df, split_strata)[, names(glb_trnobs_df)]
glb_fitobs_df <- glb_trnobs_df[!glb_trnobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ]
}
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## [1] "Newdata contains non-NA data for startprice; setting OOB to Newdata"if (!is.null(glb_max_fitobs) && (nrow(glb_fitobs_df) > glb_max_fitobs)) {
warning("glb_fitobs_df restricted to glb_max_fitobs: ",
format(glb_max_fitobs, big.mark=","))
org_fitobs_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitobs_df[split <- sample.split(org_fitobs_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitobs), ]
org_fitobs_df <- NULL
}
glb_allobs_df$.lcn <- ""
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_var)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_var)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_var)
glb_ctgry_df <- merge(newobs_ctgry_df, OOBobs_ctgry_df, by=glb_category_var
, all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}## prdline.my .n.Tst .n.OOB .freqRatio.Tst .freqRatio.OOB
## 5 iPadAir 340 340 0.1892042 0.1892042
## 3 iPad 2 295 295 0.1641625 0.1641625
## 4 iPad 3+ 289 289 0.1608236 0.1608236
## 6 iPadmini 260 260 0.1446856 0.1446856
## 7 iPadmini 2+ 219 219 0.1218698 0.1218698
## 1 Unknown 205 205 0.1140790 0.1140790
## 2 iPad 1 189 189 0.1051753 0.1051753# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 74 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## startprice startprice TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 64 UniqueID -0.009667837 TRUE 0.009667837 <NA>
## startprice startprice NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 64 1 100 FALSE FALSE FALSE FALSE
## startprice NA NA NA NA NA NA
## interaction.feat rsp_var_raw id_var rsp_var
## 64 <NA> FALSE TRUE NA
## startprice <NA> NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## [1] "D.npnct10.log" "D.npnct07.log" "D.P.http" "D.npnct02.log"
## [5] "D.npnct04.log" "D.npnct09.log" "D.npnct17.log" "D.npnct18.log"
## [9] "D.npnct19.log" "D.npnct20.log" "D.npnct21.log" "D.npnct22.log"
## [13] "D.npnct23.log" "D.npnct25.log" "D.npnct26.log" "D.npnct27.log"
## [17] "D.npnct28.log" "D.npnct29.log" "D.npnct30.log" "sold"print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)#print(setdiff(setdiff(names(glb_allobs_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 2657 67print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 860 66print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 860 66print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 1797 66print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 1797 66# # Does not handle NULL or length(glb_id_var) > 1
# glb_allobs_df$.src.trn <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_trnobs_df[, glb_id_var],
# ".src.trn"] <- 1
# glb_allobs_df$.src.fit <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_fitobs_df[, glb_id_var],
# ".src.fit"] <- 1
# glb_allobs_df$.src.OOB <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_OOBobs_df[, glb_id_var],
# ".src.OOB"] <- 1
# glb_allobs_df$.src.new <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_newobs_df[, glb_id_var],
# ".src.new"] <- 1
# #print(unique(glb_allobs_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_allobs_df <- glb_allobs_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_allobs_df
if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
rm(split)## Warning in rm(split): object 'split' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 partition.data.training 6 0 97.985 98.554 0.57
## 10 fit.models 7 0 98.555 NA NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_allobs_df),
# grep("^.src", names(glb_allobs_df), value=TRUE))
# glb_trnobs_df <- glb_allobs_df[glb_allobs_df$.src.trn == 1, keep_cols]
# glb_fitobs_df <- glb_allobs_df[glb_allobs_df$.src.fit == 1, keep_cols]
# glb_OOBobs_df <- glb_allobs_df[glb_allobs_df$.src.OOB == 1, keep_cols]
# glb_newobs_df <- glb_allobs_df[glb_allobs_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl(model_id="Baseline",
model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.lm"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -129.53 -108.00 -29.01 69.04 547.46
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 127.444 4.474 28.487 <2e-16 ***
## .rnorm -1.111 4.462 -0.249 0.803
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 131.2 on 858 degrees of freedom
## Multiple R-squared: 7.226e-05, Adjusted R-squared: -0.001093
## F-statistic: 0.06201 on 1 and 858 DF, p-value: 0.8034
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO.lm lm .rnorm 0 0.513
## min.elapsedtime.final max.R.sq.fit min.RMSE.fit max.R.sq.OOB
## 1 0.003 7.226357e-05 131.0399 0.0001316983
## min.RMSE.OOB max.Adj.R.sq.fit
## 1 212.9262 -0.001093153if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: biddable, prdline.my.fctr"## Loading required package: rpart## Fitting cp = 0.229 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 860
##
## CP nsplit rel error
## 1 0.229411 0 1
##
## Node number 1: 860 observations
## mean=127.4371, MSE=17172.71
##
## n= 860
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 860 14768530 127.4371 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats
## 1 Max.cor.Y.cv.0.rpart rpart biddable, prdline.my.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.649 0.015
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## 1 0 131.0447 0 212.9402ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: biddable, prdline.my.fctr"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 860
##
## CP nsplit rel error
## 1 2.294110e-01 0 1.0000000
## 2 8.271687e-02 1 0.7705890
## 3 8.034499e-02 2 0.6878721
## 4 4.511997e-02 3 0.6075271
## 5 2.018735e-02 4 0.5624072
## 6 2.004163e-02 5 0.5422198
## 7 6.459770e-03 6 0.5221782
## 8 3.727090e-03 7 0.5157184
## 9 2.115310e-03 8 0.5119913
## 10 1.441852e-03 9 0.5098760
## 11 6.512440e-04 10 0.5084341
## 12 8.495501e-05 11 0.5077829
## 13 7.035010e-05 12 0.5076979
## 14 0.000000e+00 13 0.5076276
##
## Variable importance
## biddable prdline.my.fctriPadAir
## 47 33
## prdline.my.fctriPadmini 2+ prdline.my.fctriPad 1
## 13 4
## prdline.my.fctriPad 3+ prdline.my.fctriPad 2
## 1 1
##
## Node number 1: 860 observations, complexity param=0.229411
## mean=127.4371, MSE=17172.71
## left son=2 (640 obs) right son=3 (220 obs)
## Primary splits:
## biddable < 0.5 to the right, improve=0.22941100, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.14781390, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.05938979, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.04145277, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.03061258, (0 missing)
##
## Node number 2: 640 observations, complexity param=0.08034499
## mean=90.63711, MSE=11139.65
## left son=4 (527 obs) right son=5 (113 obs)
## Primary splits:
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.166435000, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.041416060, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.026974120, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.021240580, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.008701147, (0 missing)
##
## Node number 3: 220 observations, complexity param=0.08271687
## mean=234.4917, MSE=19323.14
## left son=6 (183 obs) right son=7 (37 obs)
## Primary splits:
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.28736310, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.17624070, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.09007232, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.04780316, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.01981494, (0 missing)
##
## Node number 4: 527 observations, complexity param=0.02018735
## mean=70.69863, MSE=5602.216
## left son=8 (474 obs) right son=9 (53 obs)
## Primary splits:
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.100982500, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.038684240, (0 missing)
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.015093930, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.014567220, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.000390718, (0 missing)
##
## Node number 5: 113 observations
## mean=183.6245, MSE=26463.98
##
## Node number 6: 183 observations, complexity param=0.04511997
## mean=200.9851, MSE=13424.58
## left son=12 (156 obs) right son=13 (27 obs)
## Primary splits:
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.271240400, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.189593800, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.025460260, (0 missing)
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.004209899, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.002555074, (0 missing)
##
## Node number 7: 37 observations
## mean=400.2132, MSE=15480.72
##
## Node number 8: 474 observations, complexity param=0.00645977
## mean=62.74525, MSE=4145.721
## left son=16 (365 obs) right son=17 (109 obs)
## Primary splits:
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.048548510, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.033084020, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.006462783, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the left, improve=0.001514746, (0 missing)
##
## Node number 9: 53 observations
## mean=141.8289, MSE=13003
##
## Node number 12: 156 observations, complexity param=0.02004163
## mean=175.8809, MSE=9753.424
## left son=24 (29 obs) right son=25 (127 obs)
## Primary splits:
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.194530900, (0 missing)
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.049943280, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.005997696, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the left, improve=0.004713566, (0 missing)
##
## Node number 13: 27 observations
## mean=346.0319, MSE=9955.85
##
## Node number 16: 365 observations, complexity param=0.00211531
## mean=54.99252, MSE=3182.492
## left son=32 (96 obs) right son=33 (269 obs)
## Primary splits:
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.026893730, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the left, improve=0.018654610, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.000280386, (0 missing)
##
## Node number 17: 109 observations
## mean=88.70624, MSE=6495.969
##
## Node number 24: 29 observations
## mean=84.7269, MSE=1785.663
##
## Node number 25: 127 observations, complexity param=0.00372709
## mean=196.6956, MSE=9242.241
## left son=50 (32 obs) right son=51 (95 obs)
## Primary splits:
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.046894960, (0 missing)
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.016244160, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.002513227, (0 missing)
##
## Node number 32: 96 observations
## mean=39.50615, MSE=1380.205
##
## Node number 33: 269 observations, complexity param=0.000651244
## mean=60.51926, MSE=3709.554
## left son=66 (115 obs) right son=67 (154 obs)
## Primary splits:
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.009638447, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the left, improve=0.007920794, (0 missing)
## Surrogate splits:
## prdline.my.fctriPadmini < 0.5 to the left, agree=0.796, adj=0.522, (0 split)
##
## Node number 50: 32 observations
## mean=160.825, MSE=2119.278
##
## Node number 51: 95 observations, complexity param=0.001441852
## mean=208.7783, MSE=11062.15
## left son=102 (34 obs) right son=103 (61 obs)
## Primary splits:
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.020262580, (0 missing)
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.003018897, (0 missing)
## Surrogate splits:
## prdline.my.fctriPad 3+ < 0.5 to the left, agree=0.737, adj=0.265, (0 split)
##
## Node number 66: 115 observations
## mean=53.59974, MSE=2373.183
##
## Node number 67: 154 observations, complexity param=7.03501e-05
## mean=65.68643, MSE=4645.039
## left son=134 (55 obs) right son=135 (99 obs)
## Primary splits:
## prdline.my.fctriPadmini < 0.5 to the left, improve=0.001452419, (0 missing)
##
## Node number 102: 34 observations
## mean=188.7247, MSE=5781.186
##
## Node number 103: 61 observations, complexity param=8.495501e-05
## mean=219.9557, MSE=13656.55
## left son=206 (36 obs) right son=207 (25 obs)
## Primary splits:
## prdline.my.fctriPad 3+ < 0.5 to the right, improve=0.001506105, (0 missing)
##
## Node number 134: 55 observations
## mean=62.20164, MSE=6081.402
##
## Node number 135: 99 observations
## mean=67.62242, MSE=3836.565
##
## Node number 206: 36 observations
## mean=216.1764, MSE=4876.6
##
## Node number 207: 25 observations
## mean=225.398, MSE=26249.5
##
## n= 860
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 860 14768530.00 127.43710
## 2) biddable>=0.5 640 7129375.00 90.63711
## 4) prdline.my.fctriPadAir< 0.5 527 2952368.00 70.69863
## 8) prdline.my.fctriPadmini 2+< 0.5 474 1965072.00 62.74525
## 16) prdline.my.fctriPad 3+< 0.5 365 1161610.00 54.99252
## 32) prdline.my.fctriPad 1>=0.5 96 132499.70 39.50615 *
## 33) prdline.my.fctriPad 1< 0.5 269 997870.00 60.51926
## 66) prdline.my.fctriPad 2>=0.5 115 272916.00 53.59974 *
## 67) prdline.my.fctriPad 2< 0.5 154 715336.10 65.68643
## 134) prdline.my.fctriPadmini< 0.5 55 334477.10 62.20164 *
## 135) prdline.my.fctriPadmini>=0.5 99 379820.00 67.62242 *
## 17) prdline.my.fctriPad 3+>=0.5 109 708060.70 88.70624 *
## 9) prdline.my.fctriPadmini 2+>=0.5 53 689158.80 141.82890 *
## 5) prdline.my.fctriPadAir>=0.5 113 2990430.00 183.62450 *
## 3) biddable< 0.5 220 4251091.00 234.49170
## 6) prdline.my.fctriPadAir< 0.5 183 2456698.00 200.98510
## 12) prdline.my.fctriPadmini 2+< 0.5 156 1521534.00 175.88090
## 24) prdline.my.fctriPad 1>=0.5 29 51784.21 84.72690 *
## 25) prdline.my.fctriPad 1< 0.5 127 1173765.00 196.69560
## 50) prdline.my.fctriPad 2>=0.5 32 67816.89 160.82500 *
## 51) prdline.my.fctriPad 2< 0.5 95 1050904.00 208.77830
## 102) prdline.my.fctriPadmini>=0.5 34 196560.30 188.72470 *
## 103) prdline.my.fctriPadmini< 0.5 61 833049.70 219.95570
## 206) prdline.my.fctriPad 3+>=0.5 36 175557.60 216.17640 *
## 207) prdline.my.fctriPad 3+< 0.5 25 656237.40 225.39800 *
## 13) prdline.my.fctriPadmini 2+>=0.5 27 268808.00 346.03190 *
## 7) prdline.my.fctriPadAir>=0.5 37 572786.80 400.21320 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart biddable, prdline.my.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.478 0.009
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.4923724 93.3667 0.5489639 143.009if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: biddable, prdline.my.fctr"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0803 on full training set## Warning in myfit_mdl(model_id = "Max.cor.Y", model_method = "rpart",
## model_type = glb_model_type, : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 860
##
## CP nsplit rel error
## 1 0.22941102 0 1.0000000
## 2 0.08271687 1 0.7705890
## 3 0.08034499 2 0.6878721
##
## Variable importance
## biddable prdline.my.fctriPadAir
## 73 27
##
## Node number 1: 860 observations, complexity param=0.229411
## mean=127.4371, MSE=17172.71
## left son=2 (640 obs) right son=3 (220 obs)
## Primary splits:
## biddable < 0.5 to the right, improve=0.22941100, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.14781390, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.05938979, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.04145277, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.03061258, (0 missing)
##
## Node number 2: 640 observations
## mean=90.63711, MSE=11139.65
##
## Node number 3: 220 observations, complexity param=0.08271687
## mean=234.4917, MSE=19323.14
## left son=6 (183 obs) right son=7 (37 obs)
## Primary splits:
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.28736310, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.17624070, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.09007232, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.04780316, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.01981494, (0 missing)
##
## Node number 6: 183 observations
## mean=200.9851, MSE=13424.58
##
## Node number 7: 37 observations
## mean=400.2132, MSE=15480.72
##
## n= 860
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 860 14768530.0 127.43710
## 2) biddable>=0.5 640 7129375.0 90.63711 *
## 3) biddable< 0.5 220 4251091.0 234.49170
## 6) prdline.my.fctriPadAir< 0.5 183 2456698.0 200.98510 *
## 7) prdline.my.fctriPadAir>=0.5 37 572786.8 400.21320 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart biddable, prdline.my.fctr 3
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 1.031 0.012 0.3121279
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit min.RMSESD.fit
## 1 111.8385 0.450545 157.8425 0.2750573 3.592112
## max.RsquaredSD.fit
## 1 0.04148092# Used to compare vs. Interactions.High.cor.Y and/or Max.cor.Y.TmSrs
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.lm"
## [1] " indep_vars: biddable, prdline.my.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -283.21 -61.33 -5.70 47.57 447.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 209.168 12.015 17.409 < 2e-16 ***
## biddable -139.589 7.591 -18.389 < 2e-16 ***
## `prdline.my.fctriPad 1` -51.966 13.873 -3.746 0.000192 ***
## `prdline.my.fctriPad 2` -23.024 13.468 -1.710 0.087716 .
## `prdline.my.fctriPad 3+` 16.118 13.490 1.195 0.232495
## prdline.my.fctriPadAir 133.039 13.411 9.920 < 2e-16 ***
## prdline.my.fctriPadmini -6.682 13.703 -0.488 0.625913
## `prdline.my.fctriPadmini 2+` 94.057 15.307 6.145 1.23e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 96.8 on 852 degrees of freedom
## Multiple R-squared: 0.4594, Adjusted R-squared: 0.455
## F-statistic: 103.4 on 7 and 852 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.lm lm biddable, prdline.my.fctr 1
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.96 0.005 0.459417
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit max.Rsquared.fit
## 1 97.12892 0.5186351 147.7389 0.4549756 0.452455
## min.RMSESD.fit max.RsquaredSD.fit
## 1 3.422758 0.04130826if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_vars, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.lm"
## [1] " indep_vars: biddable, prdline.my.fctr, biddable:D.TfIdf.sum.post.stop, biddable:D.npnct06.log, biddable:D.npnct03.log, biddable:D.terms.n.post.stem, biddable:D.nuppr.log, biddable:D.nwrds.unq.log, biddable:D.npnct24.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.TfIdf.sum.post.stem"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -283.58 -56.94 -3.48 47.92 436.40
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 212.30 11.98 17.718 < 2e-16
## biddable -222.35 70.59 -3.150 0.00169
## `prdline.my.fctriPad 1` -56.08 13.88 -4.040 5.84e-05
## `prdline.my.fctriPad 2` -25.39 13.44 -1.889 0.05924
## `prdline.my.fctriPad 3+` 13.13 13.44 0.977 0.32897
## prdline.my.fctriPadAir 130.28 13.37 9.741 < 2e-16
## prdline.my.fctriPadmini -10.90 13.75 -0.793 0.42824
## `prdline.my.fctriPadmini 2+` 88.82 15.27 5.817 8.50e-09
## `biddable:D.TfIdf.sum.post.stop` -29.65 22.01 -1.347 0.17829
## `biddable:D.npnct06.log` 63.66 44.52 1.430 0.15311
## `biddable:D.npnct03.log` -46.76 53.72 -0.870 0.38433
## `biddable:D.terms.n.post.stem` -15.53 10.33 -1.504 0.13287
## `biddable:D.nuppr.log` -17.08 38.01 -0.449 0.65327
## `biddable:D.nwrds.unq.log` 148.64 98.64 1.507 0.13219
## `biddable:D.npnct24.log` -100.13 131.02 -0.764 0.44493
## `biddable:D.ratio.nstopwrds.nwrds` 93.37 70.13 1.331 0.18345
## `biddable:D.TfIdf.sum.post.stem` 26.85 22.96 1.169 0.24256
##
## (Intercept) ***
## biddable **
## `prdline.my.fctriPad 1` ***
## `prdline.my.fctriPad 2` .
## `prdline.my.fctriPad 3+`
## prdline.my.fctriPadAir ***
## prdline.my.fctriPadmini
## `prdline.my.fctriPadmini 2+` ***
## `biddable:D.TfIdf.sum.post.stop`
## `biddable:D.npnct06.log`
## `biddable:D.npnct03.log`
## `biddable:D.terms.n.post.stem`
## `biddable:D.nuppr.log`
## `biddable:D.nwrds.unq.log`
## `biddable:D.npnct24.log`
## `biddable:D.ratio.nstopwrds.nwrds`
## `biddable:D.TfIdf.sum.post.stem`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 96.04 on 843 degrees of freedom
## Multiple R-squared: 0.4735, Adjusted R-squared: 0.4635
## F-statistic: 47.38 on 16 and 843 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 Interact.High.cor.Y.lm lm
## feats
## 1 biddable, prdline.my.fctr, biddable:D.TfIdf.sum.post.stop, biddable:D.npnct06.log, biddable:D.npnct03.log, biddable:D.terms.n.post.stem, biddable:D.nuppr.log, biddable:D.nwrds.unq.log, biddable:D.npnct24.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.TfIdf.sum.post.stem
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.97 0.009
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.4734947 96.62961 0.5214589 147.305 0.4635018
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.4579098 3.73388 0.04250557# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !myNearZV &
(exclude.as.feat != 1))[, "id"]
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":",
feat))
return(vars_vctr)
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.lm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct12.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -295.09 -43.37 -2.61 47.40 346.65
##
## Coefficients: (8 not defined because of singularities)
## Estimate Std. Error
## (Intercept) 1.749e+02 1.898e+02
## `prdline.my.fctriPad 1` -5.301e+01 1.883e+01
## `prdline.my.fctriPad 2` -1.601e+01 1.826e+01
## `prdline.my.fctriPad 3+` 1.811e+01 1.804e+01
## prdline.my.fctriPadAir 1.154e+02 1.814e+01
## prdline.my.fctriPadmini 2.930e-01 1.815e+01
## `prdline.my.fctriPadmini 2+` 5.161e+01 1.941e+01
## `condition.fctrFor parts or not working` -4.619e+01 1.233e+01
## `condition.fctrManufacturer refurbished` -1.207e+01 2.384e+01
## condition.fctrNew 6.611e+01 1.180e+01
## `condition.fctrNew other (see details)` 4.048e+01 1.624e+01
## `condition.fctrSeller refurbished` -2.520e+01 1.582e+01
## color.fctrBlack 5.582e+00 8.248e+00
## color.fctrGold 7.656e+00 2.120e+01
## `color.fctrSpace Gray` 2.093e+01 1.098e+01
## color.fctrWhite 2.665e+01 8.233e+00
## D.TfIdf.sum.stem.stop.Ratio 2.056e+02 1.152e+02
## `carrier.fctrAT&T` -4.265e+01 2.362e+01
## carrier.fctrOther 7.421e+01 5.832e+01
## carrier.fctrSprint -8.905e+01 3.259e+01
## `carrier.fctrT-Mobile` -4.363e+01 3.821e+01
## carrier.fctrUnknown -3.128e+01 1.753e+01
## carrier.fctrVerizon -3.457e+01 2.426e+01
## storage.fctr16 -1.336e+02 1.976e+01
## storage.fctr32 -1.208e+02 2.065e+01
## storage.fctr64 -8.514e+01 2.039e+01
## storage.fctrUnknown -9.303e+01 2.667e+01
## D.npnct14.log 1.142e+00 3.762e+01
## D.terms.n.stem.stop.Ratio -4.595e+01 1.640e+02
## cellular.fctr1 4.545e+01 2.146e+01
## cellular.fctrUnknown NA NA
## D.ndgts.log 2.593e+00 1.435e+01
## .rnorm -2.432e-01 3.013e+00
## idseq.my -1.131e-02 7.438e-03
## D.npnct08.log 1.466e+01 2.347e+01
## D.npnct05.log -1.137e+02 7.140e+01
## D.npnct15.log -2.335e+01 3.111e+01
## D.npnct01.log 2.101e+00 2.028e+01
## D.npnct12.log 1.820e+01 2.340e+01
## D.npnct03.log 1.878e+01 3.837e+01
## D.npnct11.log -2.276e+01 1.155e+01
## D.npnct13.log -1.308e+01 1.063e+01
## D.TfIdf.sum.post.stop 2.908e+00 2.345e+00
## D.ratio.sum.TfIdf.nwrds -3.131e+01 6.813e+00
## biddable -1.379e+02 7.362e+00
## `prdline.my.fctrUnknown:.clusterid.fctr2` 7.962e+01 2.373e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.730e+01 2.563e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 2.436e+01 2.066e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 1.408e+01 2.736e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -5.041e+01 2.200e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 3.076e+01 2.678e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 6.549e+01 3.182e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` 5.568e+01 4.226e+01
## `prdline.my.fctriPad 1:.clusterid.fctr3` 4.391e+01 2.780e+01
## `prdline.my.fctriPad 2:.clusterid.fctr3` -8.535e+00 4.206e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -3.239e+00 2.490e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -2.441e+01 3.229e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.312e+01 2.406e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -9.485e+00 3.281e+01
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 3.480e+01 3.431e+01
## `prdline.my.fctriPad 2:.clusterid.fctr4` 4.090e+00 2.860e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 2.455e+01 2.593e+01
## `prdline.my.fctriPadAir:.clusterid.fctr4` -1.455e+01 3.305e+01
## `prdline.my.fctriPadmini:.clusterid.fctr4` -4.123e+00 3.074e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.357e+01 3.338e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.653e+01 4.621e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## t value Pr(>|t|)
## (Intercept) 0.922 0.356933
## `prdline.my.fctriPad 1` -2.815 0.005001 **
## `prdline.my.fctriPad 2` -0.877 0.380840
## `prdline.my.fctriPad 3+` 1.004 0.315545
## prdline.my.fctriPadAir 6.366 3.28e-10 ***
## prdline.my.fctriPadmini 0.016 0.987123
## `prdline.my.fctriPadmini 2+` 2.660 0.007976 **
## `condition.fctrFor parts or not working` -3.746 0.000193 ***
## `condition.fctrManufacturer refurbished` -0.506 0.612706
## condition.fctrNew 5.600 2.96e-08 ***
## `condition.fctrNew other (see details)` 2.493 0.012869 *
## `condition.fctrSeller refurbished` -1.593 0.111540
## color.fctrBlack 0.677 0.498730
## color.fctrGold 0.361 0.718079
## `color.fctrSpace Gray` 1.906 0.056946 .
## color.fctrWhite 3.236 0.001261 **
## D.TfIdf.sum.stem.stop.Ratio 1.784 0.074787 .
## `carrier.fctrAT&T` -1.805 0.071397 .
## carrier.fctrOther 1.272 0.203596
## carrier.fctrSprint -2.732 0.006427 **
## `carrier.fctrT-Mobile` -1.142 0.253891
## carrier.fctrUnknown -1.784 0.074780 .
## carrier.fctrVerizon -1.425 0.154511
## storage.fctr16 -6.762 2.64e-11 ***
## storage.fctr32 -5.850 7.17e-09 ***
## storage.fctr64 -4.176 3.30e-05 ***
## storage.fctrUnknown -3.489 0.000512 ***
## D.npnct14.log 0.030 0.975792
## D.terms.n.stem.stop.Ratio -0.280 0.779413
## cellular.fctr1 2.118 0.034465 *
## cellular.fctrUnknown NA NA
## D.ndgts.log 0.181 0.856700
## .rnorm -0.081 0.935698
## idseq.my -1.520 0.128852
## D.npnct08.log 0.625 0.532450
## D.npnct05.log -1.593 0.111566
## D.npnct15.log -0.751 0.453149
## D.npnct01.log 0.104 0.917486
## D.npnct12.log 0.778 0.437066
## D.npnct03.log 0.489 0.624710
## D.npnct11.log -1.970 0.049169 *
## D.npnct13.log -1.231 0.218864
## D.TfIdf.sum.post.stop 1.240 0.215370
## D.ratio.sum.TfIdf.nwrds -4.595 5.03e-06 ***
## biddable -18.736 < 2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 3.356 0.000830 ***
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.675 0.499854
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.179 0.238717
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.514 0.607071
## `prdline.my.fctriPadAir:.clusterid.fctr2` -2.291 0.022221 *
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.149 0.251068
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 2.058 0.039918 *
## `prdline.my.fctrUnknown:.clusterid.fctr3` 1.317 0.188082
## `prdline.my.fctriPad 1:.clusterid.fctr3` 1.579 0.114686
## `prdline.my.fctriPad 2:.clusterid.fctr3` -0.203 0.839253
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.130 0.896507
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.756 0.449823
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.545 0.585916
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.289 0.772571
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 1.014 0.310751
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.143 0.886352
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.947 0.344074
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.440 0.659920
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.134 0.893342
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.706 0.480414
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.358 0.720606
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 85.42 on 795 degrees of freedom
## Multiple R-squared: 0.6072, Adjusted R-squared: 0.5756
## F-statistic: 19.2 on 64 and 795 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
## model_id model_method
## 1 Low.cor.X.lm lm
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct12.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.123 0.032
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.6072324 92.54651 0.6201211 131.2443 0.5756134
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.5096123 3.032874 0.032838rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 7 0 98.555 114.633 16.078
## 11 fit.models 7 1 114.634 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn")## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 116.773 NA NA# Options:
# 1. rpart & rf manual tuning
# 2. rf without pca (default: with pca)
#stop(here"); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
#glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
# All X that is not user excluded
for (model_id_pfx in c("All.X", "All.Interact.X")) {
#model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
if (model_id_pfx == "All.Interact.X") {
interact_vars_vctr <- c(
#"startprice.log",
#"startprice.diff",
"biddable", "idseq.my")
indep_vars_vctr <- union(setdiff(indep_vars_vctr, interact_vars_vctr),
paste(glb_category_var, interact_vars_vctr, sep=".fctr*"))
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
#stop(here")
for (method in glb_models_method_vctr) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", method), major.inc=TRUE)
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# If All.X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(model_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.nuppr.log, glb_allobs_df$A.nuppr.log)
# all.equal(glb_allobs_df$S.npnct19.log, glb_allobs_df$A.npnct19.log)
# all.equal(glb_allobs_df$S.P.year.colon, glb_allobs_df$A.P.year.colon)
# all.equal(glb_allobs_df$S.T.share, glb_allobs_df$A.T.share)
# all.equal(glb_allobs_df$H.T.clip, glb_allobs_df$H.P.daily.clip.report)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# dsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# dsp_obs(Abstract.contains="[Ss]hare", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}
}## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 116.773 116.782 0.009
## 2 fit.models_1_lm 2 0 116.782 NA NA
## [1] "fitting model: All.X.lm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -296.80 -44.08 -0.23 45.86 345.05
##
## Coefficients: (10 not defined because of singularities)
## Estimate Std. Error
## (Intercept) -8.695e+04 8.313e+04
## `prdline.my.fctriPad 1` -5.361e+01 1.887e+01
## `prdline.my.fctriPad 2` -1.618e+01 1.830e+01
## `prdline.my.fctriPad 3+` 1.474e+01 1.810e+01
## prdline.my.fctriPadAir 1.155e+02 1.816e+01
## prdline.my.fctriPadmini 2.398e-02 1.816e+01
## `prdline.my.fctriPadmini 2+` 5.024e+01 1.941e+01
## `condition.fctrFor parts or not working` -4.889e+01 1.241e+01
## `condition.fctrManufacturer refurbished` -1.170e+01 2.381e+01
## condition.fctrNew 6.422e+01 1.181e+01
## `condition.fctrNew other (see details)` 4.267e+01 1.663e+01
## `condition.fctrSeller refurbished` -2.370e+01 1.636e+01
## color.fctrBlack 5.275e+00 8.322e+00
## color.fctrGold 1.064e+01 2.130e+01
## `color.fctrSpace Gray` 2.080e+01 1.104e+01
## color.fctrWhite 2.793e+01 8.331e+00
## D.ratio.nstopwrds.nwrds -5.351e+01 2.506e+02
## D.TfIdf.sum.stem.stop.Ratio 3.948e+02 5.920e+02
## `carrier.fctrAT&T` -4.334e+01 2.370e+01
## carrier.fctrOther 7.476e+01 5.926e+01
## carrier.fctrSprint -9.364e+01 3.283e+01
## `carrier.fctrT-Mobile` -3.889e+01 3.834e+01
## carrier.fctrUnknown -2.991e+01 1.758e+01
## carrier.fctrVerizon -3.623e+01 2.439e+01
## storage.fctr16 -1.348e+02 1.975e+01
## storage.fctr32 -1.235e+02 2.068e+01
## storage.fctr64 -8.767e+01 2.039e+01
## storage.fctrUnknown -9.926e+01 2.674e+01
## D.npnct14.log 1.401e+01 3.900e+01
## D.terms.n.stem.stop.Ratio 8.695e+04 8.316e+04
## cellular.fctr1 4.527e+01 2.150e+01
## cellular.fctrUnknown NA NA
## D.ndgts.log -6.684e+00 1.900e+01
## .rnorm 2.452e-01 3.030e+00
## idseq.my -1.281e-02 7.497e-03
## D.npnct08.log 1.729e+01 2.381e+01
## D.npnct05.log -9.226e+01 7.318e+01
## D.npnct15.log -7.132e+00 3.215e+01
## D.npnct01.log 2.954e+01 2.292e+01
## D.npnct16.log -5.249e+00 6.595e+01
## D.npnct12.log 8.178e+00 2.464e+01
## D.npnct06.log 8.081e+01 7.740e+01
## D.npnct03.log -4.310e+00 5.230e+01
## D.nstopwrds.log -5.405e+01 7.585e+01
## D.npnct11.log -2.372e+01 1.298e+01
## D.npnct13.log -8.482e+00 1.334e+01
## D.terms.n.post.stop -6.178e+02 5.691e+02
## D.terms.n.post.stem 6.154e+02 5.707e+02
## D.nwrds.log 2.225e+02 9.333e+01
## D.terms.n.post.stop.log 9.758e+04 9.314e+04
## D.nwrds.unq.log -9.764e+04 9.316e+04
## D.terms.n.post.stem.log NA NA
## D.nchrs.log -3.636e+02 1.713e+02
## D.nuppr.log 3.204e+02 1.500e+02
## D.npnct24.log -1.750e+02 2.135e+02
## D.TfIdf.sum.post.stem -3.912e+01 9.373e+01
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 3.929e+01 8.962e+01
## D.ratio.sum.TfIdf.nwrds -5.383e+00 1.779e+01
## biddable -1.385e+02 7.403e+00
## `prdline.my.fctrUnknown:.clusterid.fctr2` 9.521e+01 2.620e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.499e+01 2.649e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 2.641e+01 2.165e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 1.784e+01 2.850e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -4.649e+01 2.296e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 3.743e+01 2.842e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 6.663e+01 3.316e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` 5.250e+01 4.290e+01
## `prdline.my.fctriPad 1:.clusterid.fctr3` 4.190e+01 2.893e+01
## `prdline.my.fctriPad 2:.clusterid.fctr3` 2.903e+00 4.354e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -6.886e-01 2.568e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -3.146e+01 3.308e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.820e+01 2.566e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 1.620e+01 3.466e+01
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 2.636e+01 3.563e+01
## `prdline.my.fctriPad 2:.clusterid.fctr4` 9.993e+00 3.078e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 2.753e+01 2.691e+01
## `prdline.my.fctriPadAir:.clusterid.fctr4` -1.153e+01 3.482e+01
## `prdline.my.fctriPadmini:.clusterid.fctr4` -3.009e+00 3.234e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 3.685e+01 3.491e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.401e+01 4.754e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## t value Pr(>|t|)
## (Intercept) -1.046 0.295907
## `prdline.my.fctriPad 1` -2.841 0.004613 **
## `prdline.my.fctriPad 2` -0.884 0.377062
## `prdline.my.fctriPad 3+` 0.814 0.415678
## prdline.my.fctriPadAir 6.358 3.47e-10 ***
## prdline.my.fctriPadmini 0.001 0.998946
## `prdline.my.fctriPadmini 2+` 2.588 0.009828 **
## `condition.fctrFor parts or not working` -3.940 8.87e-05 ***
## `condition.fctrManufacturer refurbished` -0.491 0.623453
## condition.fctrNew 5.436 7.29e-08 ***
## `condition.fctrNew other (see details)` 2.565 0.010495 *
## `condition.fctrSeller refurbished` -1.449 0.147767
## color.fctrBlack 0.634 0.526354
## color.fctrGold 0.499 0.617610
## `color.fctrSpace Gray` 1.883 0.060089 .
## color.fctrWhite 3.352 0.000841 ***
## D.ratio.nstopwrds.nwrds -0.214 0.830971
## D.TfIdf.sum.stem.stop.Ratio 0.667 0.505067
## `carrier.fctrAT&T` -1.829 0.067763 .
## carrier.fctrOther 1.262 0.207501
## carrier.fctrSprint -2.853 0.004448 **
## `carrier.fctrT-Mobile` -1.014 0.310757
## carrier.fctrUnknown -1.701 0.089295 .
## carrier.fctrVerizon -1.486 0.137798
## storage.fctr16 -6.826 1.75e-11 ***
## storage.fctr32 -5.975 3.49e-09 ***
## storage.fctr64 -4.298 1.94e-05 ***
## storage.fctrUnknown -3.712 0.000220 ***
## D.npnct14.log 0.359 0.719567
## D.terms.n.stem.stop.Ratio 1.046 0.296105
## cellular.fctr1 2.105 0.035605 *
## cellular.fctrUnknown NA NA
## D.ndgts.log -0.352 0.725047
## .rnorm 0.081 0.935539
## idseq.my -1.709 0.087830 .
## D.npnct08.log 0.726 0.468089
## D.npnct05.log -1.261 0.207797
## D.npnct15.log -0.222 0.824532
## D.npnct01.log 1.289 0.197718
## D.npnct16.log -0.080 0.936578
## D.npnct12.log 0.332 0.740059
## D.npnct06.log 1.044 0.296798
## D.npnct03.log -0.082 0.934339
## D.nstopwrds.log -0.713 0.476355
## D.npnct11.log -1.828 0.067984 .
## D.npnct13.log -0.636 0.525039
## D.terms.n.post.stop -1.086 0.278029
## D.terms.n.post.stem 1.078 0.281196
## D.nwrds.log 2.384 0.017352 *
## D.terms.n.post.stop.log 1.048 0.295118
## D.nwrds.unq.log -1.048 0.294905
## D.terms.n.post.stem.log NA NA
## D.nchrs.log -2.123 0.034051 *
## D.nuppr.log 2.136 0.032996 *
## D.npnct24.log -0.819 0.412775
## D.TfIdf.sum.post.stem -0.417 0.676517
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 0.438 0.661210
## D.ratio.sum.TfIdf.nwrds -0.303 0.762261
## biddable -18.708 < 2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 3.633 0.000298 ***
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.566 0.571571
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.220 0.222885
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.626 0.531594
## `prdline.my.fctriPadAir:.clusterid.fctr2` -2.025 0.043233 *
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.317 0.188227
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 2.009 0.044844 *
## `prdline.my.fctrUnknown:.clusterid.fctr3` 1.224 0.221409
## `prdline.my.fctriPad 1:.clusterid.fctr3` 1.448 0.147905
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.067 0.946866
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.027 0.978613
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.951 0.341890
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.710 0.478222
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.467 0.640408
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.740 0.459669
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.325 0.745566
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.023 0.306625
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.331 0.740545
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.093 0.925879
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 1.056 0.291486
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.295 0.768282
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 85.13 on 782 degrees of freedom
## Multiple R-squared: 0.6163, Adjusted R-squared: 0.5785
## F-statistic: 16.31 on 77 and 782 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
## model_id model_method
## 1 All.X.lm lm
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.177 0.04
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.6162904 96.59996 0.5993747 134.7805 0.5785083
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.4783328 1.772347 0.01936568
## label step_major step_minor bgn end elapsed
## 2 fit.models_1_lm 2 0 116.782 119.718 2.936
## 3 fit.models_1_glm 3 0 119.719 NA NA
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -296.80 -44.08 -0.23 45.86 345.05
##
## Coefficients: (10 not defined because of singularities)
## Estimate Std. Error
## (Intercept) -8.695e+04 8.313e+04
## `prdline.my.fctriPad 1` -5.361e+01 1.887e+01
## `prdline.my.fctriPad 2` -1.618e+01 1.830e+01
## `prdline.my.fctriPad 3+` 1.474e+01 1.810e+01
## prdline.my.fctriPadAir 1.155e+02 1.816e+01
## prdline.my.fctriPadmini 2.398e-02 1.816e+01
## `prdline.my.fctriPadmini 2+` 5.024e+01 1.941e+01
## `condition.fctrFor parts or not working` -4.889e+01 1.241e+01
## `condition.fctrManufacturer refurbished` -1.170e+01 2.381e+01
## condition.fctrNew 6.422e+01 1.181e+01
## `condition.fctrNew other (see details)` 4.267e+01 1.663e+01
## `condition.fctrSeller refurbished` -2.370e+01 1.636e+01
## color.fctrBlack 5.275e+00 8.322e+00
## color.fctrGold 1.064e+01 2.130e+01
## `color.fctrSpace Gray` 2.080e+01 1.104e+01
## color.fctrWhite 2.793e+01 8.331e+00
## D.ratio.nstopwrds.nwrds -5.351e+01 2.506e+02
## D.TfIdf.sum.stem.stop.Ratio 3.948e+02 5.920e+02
## `carrier.fctrAT&T` -4.334e+01 2.370e+01
## carrier.fctrOther 7.476e+01 5.926e+01
## carrier.fctrSprint -9.364e+01 3.283e+01
## `carrier.fctrT-Mobile` -3.889e+01 3.834e+01
## carrier.fctrUnknown -2.991e+01 1.758e+01
## carrier.fctrVerizon -3.623e+01 2.439e+01
## storage.fctr16 -1.348e+02 1.975e+01
## storage.fctr32 -1.235e+02 2.068e+01
## storage.fctr64 -8.767e+01 2.039e+01
## storage.fctrUnknown -9.926e+01 2.674e+01
## D.npnct14.log 1.401e+01 3.900e+01
## D.terms.n.stem.stop.Ratio 8.695e+04 8.316e+04
## cellular.fctr1 4.527e+01 2.150e+01
## cellular.fctrUnknown NA NA
## D.ndgts.log -6.684e+00 1.900e+01
## .rnorm 2.452e-01 3.030e+00
## idseq.my -1.281e-02 7.497e-03
## D.npnct08.log 1.729e+01 2.381e+01
## D.npnct05.log -9.226e+01 7.318e+01
## D.npnct15.log -7.132e+00 3.215e+01
## D.npnct01.log 2.954e+01 2.292e+01
## D.npnct16.log -5.249e+00 6.595e+01
## D.npnct12.log 8.178e+00 2.464e+01
## D.npnct06.log 8.081e+01 7.740e+01
## D.npnct03.log -4.310e+00 5.230e+01
## D.nstopwrds.log -5.405e+01 7.585e+01
## D.npnct11.log -2.372e+01 1.298e+01
## D.npnct13.log -8.482e+00 1.334e+01
## D.terms.n.post.stop -6.178e+02 5.691e+02
## D.terms.n.post.stem 6.154e+02 5.707e+02
## D.nwrds.log 2.225e+02 9.333e+01
## D.terms.n.post.stop.log 9.758e+04 9.314e+04
## D.nwrds.unq.log -9.764e+04 9.316e+04
## D.terms.n.post.stem.log NA NA
## D.nchrs.log -3.636e+02 1.713e+02
## D.nuppr.log 3.204e+02 1.500e+02
## D.npnct24.log -1.750e+02 2.135e+02
## D.TfIdf.sum.post.stem -3.912e+01 9.373e+01
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 3.929e+01 8.962e+01
## D.ratio.sum.TfIdf.nwrds -5.383e+00 1.779e+01
## biddable -1.385e+02 7.403e+00
## `prdline.my.fctrUnknown:.clusterid.fctr2` 9.521e+01 2.620e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.499e+01 2.649e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 2.641e+01 2.165e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 1.784e+01 2.850e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -4.649e+01 2.296e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 3.743e+01 2.842e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 6.663e+01 3.316e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` 5.250e+01 4.290e+01
## `prdline.my.fctriPad 1:.clusterid.fctr3` 4.190e+01 2.893e+01
## `prdline.my.fctriPad 2:.clusterid.fctr3` 2.903e+00 4.354e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -6.886e-01 2.568e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -3.146e+01 3.308e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.820e+01 2.566e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 1.620e+01 3.466e+01
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 2.636e+01 3.563e+01
## `prdline.my.fctriPad 2:.clusterid.fctr4` 9.993e+00 3.078e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 2.753e+01 2.691e+01
## `prdline.my.fctriPadAir:.clusterid.fctr4` -1.153e+01 3.482e+01
## `prdline.my.fctriPadmini:.clusterid.fctr4` -3.009e+00 3.234e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 3.685e+01 3.491e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.401e+01 4.754e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## t value Pr(>|t|)
## (Intercept) -1.046 0.295907
## `prdline.my.fctriPad 1` -2.841 0.004613 **
## `prdline.my.fctriPad 2` -0.884 0.377062
## `prdline.my.fctriPad 3+` 0.814 0.415678
## prdline.my.fctriPadAir 6.358 3.47e-10 ***
## prdline.my.fctriPadmini 0.001 0.998946
## `prdline.my.fctriPadmini 2+` 2.588 0.009828 **
## `condition.fctrFor parts or not working` -3.940 8.87e-05 ***
## `condition.fctrManufacturer refurbished` -0.491 0.623453
## condition.fctrNew 5.436 7.29e-08 ***
## `condition.fctrNew other (see details)` 2.565 0.010495 *
## `condition.fctrSeller refurbished` -1.449 0.147767
## color.fctrBlack 0.634 0.526354
## color.fctrGold 0.499 0.617610
## `color.fctrSpace Gray` 1.883 0.060089 .
## color.fctrWhite 3.352 0.000841 ***
## D.ratio.nstopwrds.nwrds -0.214 0.830971
## D.TfIdf.sum.stem.stop.Ratio 0.667 0.505067
## `carrier.fctrAT&T` -1.829 0.067763 .
## carrier.fctrOther 1.262 0.207501
## carrier.fctrSprint -2.853 0.004448 **
## `carrier.fctrT-Mobile` -1.014 0.310757
## carrier.fctrUnknown -1.701 0.089295 .
## carrier.fctrVerizon -1.486 0.137798
## storage.fctr16 -6.826 1.75e-11 ***
## storage.fctr32 -5.975 3.49e-09 ***
## storage.fctr64 -4.298 1.94e-05 ***
## storage.fctrUnknown -3.712 0.000220 ***
## D.npnct14.log 0.359 0.719567
## D.terms.n.stem.stop.Ratio 1.046 0.296105
## cellular.fctr1 2.105 0.035605 *
## cellular.fctrUnknown NA NA
## D.ndgts.log -0.352 0.725047
## .rnorm 0.081 0.935539
## idseq.my -1.709 0.087830 .
## D.npnct08.log 0.726 0.468089
## D.npnct05.log -1.261 0.207797
## D.npnct15.log -0.222 0.824532
## D.npnct01.log 1.289 0.197718
## D.npnct16.log -0.080 0.936578
## D.npnct12.log 0.332 0.740059
## D.npnct06.log 1.044 0.296798
## D.npnct03.log -0.082 0.934339
## D.nstopwrds.log -0.713 0.476355
## D.npnct11.log -1.828 0.067984 .
## D.npnct13.log -0.636 0.525039
## D.terms.n.post.stop -1.086 0.278029
## D.terms.n.post.stem 1.078 0.281196
## D.nwrds.log 2.384 0.017352 *
## D.terms.n.post.stop.log 1.048 0.295118
## D.nwrds.unq.log -1.048 0.294905
## D.terms.n.post.stem.log NA NA
## D.nchrs.log -2.123 0.034051 *
## D.nuppr.log 2.136 0.032996 *
## D.npnct24.log -0.819 0.412775
## D.TfIdf.sum.post.stem -0.417 0.676517
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 0.438 0.661210
## D.ratio.sum.TfIdf.nwrds -0.303 0.762261
## biddable -18.708 < 2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 3.633 0.000298 ***
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.566 0.571571
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.220 0.222885
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.626 0.531594
## `prdline.my.fctriPadAir:.clusterid.fctr2` -2.025 0.043233 *
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.317 0.188227
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 2.009 0.044844 *
## `prdline.my.fctrUnknown:.clusterid.fctr3` 1.224 0.221409
## `prdline.my.fctriPad 1:.clusterid.fctr3` 1.448 0.147905
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.067 0.946866
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.027 0.978613
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.951 0.341890
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.710 0.478222
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.467 0.640408
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.740 0.459669
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.325 0.745566
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.023 0.306625
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.331 0.740545
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.093 0.925879
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 1.056 0.291486
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.295 0.768282
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 7246.581)
##
## Null deviance: 14768530 on 859 degrees of freedom
## Residual deviance: 5666826 on 782 degrees of freedom
## AIC: 10161
##
## Number of Fisher Scoring iterations: 2
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## model_id model_method
## 1 All.X.glm glm
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.07 0.057
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.6162904 96.59996 0.5993747 134.7805 10160.73
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.4783328 1.772347 0.01936568
## label step_major step_minor bgn end elapsed
## 3 fit.models_1_glm 3 0 119.719 122.661 2.942
## 4 fit.models_1_bayesglm 4 0 122.662 NA NA
## [1] "fitting model: All.X.bayesglm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr"## Loading required package: arm
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
##
## The following object is masked from 'package:tidyr':
##
## expand
##
## Loading required package: lme4
##
## Attaching package: 'lme4'
##
## The following object is masked from 'package:nlme':
##
## lmList
##
##
## arm (Version 1.8-6, built: 2015-7-7)
##
## Working directory is /Users/bbalaji-2012/Documents/Work/Courses/MIT/Analytics_Edge_15_071x/Assignments/Kaggle_eBay_iPads## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -297.09 -43.51 -0.54 45.89 344.21
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 2.037e+02 6.830e+02
## `prdline.my.fctriPad 1` -5.419e+01 1.892e+01
## `prdline.my.fctriPad 2` -1.708e+01 1.836e+01
## `prdline.my.fctriPad 3+` 1.518e+01 1.816e+01
## prdline.my.fctriPadAir 1.151e+02 1.823e+01
## prdline.my.fctriPadmini -2.507e-01 1.822e+01
## `prdline.my.fctriPadmini 2+` 4.974e+01 1.948e+01
## `condition.fctrFor parts or not working` -4.964e+01 1.246e+01
## `condition.fctrManufacturer refurbished` -1.198e+01 2.394e+01
## condition.fctrNew 6.397e+01 1.189e+01
## `condition.fctrNew other (see details)` 4.186e+01 1.672e+01
## `condition.fctrSeller refurbished` -2.538e+01 1.620e+01
## color.fctrBlack 5.602e+00 8.364e+00
## color.fctrGold 1.101e+01 2.141e+01
## `color.fctrSpace Gray` 2.144e+01 1.108e+01
## color.fctrWhite 2.802e+01 8.344e+00
## D.ratio.nstopwrds.nwrds -5.916e+00 2.173e+02
## D.TfIdf.sum.stem.stop.Ratio 1.924e+02 4.052e+02
## `carrier.fctrAT&T` -1.603e+01 1.717e+02
## carrier.fctrOther 1.004e+02 1.779e+02
## carrier.fctrSprint -6.604e+01 1.728e+02
## `carrier.fctrT-Mobile` -1.268e+01 1.735e+02
## carrier.fctrUnknown -2.988e+00 1.717e+02
## carrier.fctrVerizon -9.707e+00 1.718e+02
## storage.fctr16 -1.340e+02 1.981e+01
## storage.fctr32 -1.223e+02 2.075e+01
## storage.fctr64 -8.652e+01 2.047e+01
## storage.fctrUnknown -9.837e+01 2.682e+01
## D.npnct14.log 1.466e+01 3.894e+01
## D.terms.n.stem.stop.Ratio -5.133e+01 5.095e+02
## cellular.fctr1 1.809e+01 1.715e+02
## cellular.fctrUnknown -2.711e+01 1.724e+02
## D.ndgts.log -6.494e+00 1.868e+01
## .rnorm 5.957e-02 3.047e+00
## idseq.my -1.226e-02 7.534e-03
## D.npnct08.log 1.619e+01 2.389e+01
## D.npnct05.log -9.357e+01 7.270e+01
## D.npnct15.log -8.651e+00 3.215e+01
## D.npnct01.log 2.796e+01 2.263e+01
## D.npnct16.log -3.955e+00 6.486e+01
## D.npnct12.log 8.715e+00 2.456e+01
## D.npnct06.log 7.702e+01 7.595e+01
## D.npnct03.log -6.100e+00 5.200e+01
## D.nstopwrds.log -6.034e+01 6.750e+01
## D.npnct11.log -2.501e+01 1.293e+01
## D.npnct13.log -9.956e+00 1.318e+01
## D.terms.n.post.stop -1.974e+01 6.644e+01
## D.terms.n.post.stem 1.695e+01 6.734e+01
## D.nwrds.log 2.053e+02 8.835e+01
## D.terms.n.post.stop.log 1.523e+01 4.313e+02
## D.nwrds.unq.log -2.985e+01 4.567e+02
## D.terms.n.post.stem.log -2.985e+01 4.567e+02
## D.nchrs.log -3.118e+02 1.564e+02
## D.nuppr.log 2.767e+02 1.377e+02
## D.npnct24.log -1.744e+02 1.906e+02
## D.TfIdf.sum.post.stem -4.261e+00 3.836e+02
## D.sum.TfIdf -4.261e+00 3.836e+02
## D.TfIdf.sum.post.stop 9.660e+00 6.255e+01
## D.ratio.sum.TfIdf.nwrds -5.577e+00 1.691e+01
## biddable -1.388e+02 7.439e+00
## `prdline.my.fctrUnknown:.clusterid.fctr2` 9.496e+01 2.609e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.542e+01 2.658e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 2.593e+01 2.145e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 1.728e+01 2.857e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -4.636e+01 2.302e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 3.388e+01 2.833e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 6.893e+01 3.282e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` 5.268e+01 4.286e+01
## `prdline.my.fctriPad 1:.clusterid.fctr3` 4.237e+01 2.896e+01
## `prdline.my.fctriPad 2:.clusterid.fctr3` 4.418e+00 4.348e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -1.783e+00 2.577e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -3.028e+01 3.316e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.683e+01 2.573e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 1.275e+01 3.458e+01
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000e+00 6.611e+02
## `prdline.my.fctriPad 1:.clusterid.fctr4` 2.656e+01 3.568e+01
## `prdline.my.fctriPad 2:.clusterid.fctr4` 1.030e+01 3.078e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 2.727e+01 2.679e+01
## `prdline.my.fctriPadAir:.clusterid.fctr4` -1.160e+01 3.477e+01
## `prdline.my.fctriPadmini:.clusterid.fctr4` -3.866e+00 3.241e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000e+00 6.611e+02
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000e+00 6.611e+02
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000e+00 6.611e+02
## `prdline.my.fctriPad 2:.clusterid.fctr5` 3.756e+01 3.493e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000e+00 6.611e+02
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000e+00 6.611e+02
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.181e+01 4.750e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000e+00 6.611e+02
## t value Pr(>|t|)
## (Intercept) 0.298 0.765595
## `prdline.my.fctriPad 1` -2.864 0.004291 **
## `prdline.my.fctriPad 2` -0.931 0.352344
## `prdline.my.fctriPad 3+` 0.836 0.403287
## prdline.my.fctriPadAir 6.313 4.61e-10 ***
## prdline.my.fctriPadmini -0.014 0.989029
## `prdline.my.fctriPadmini 2+` 2.553 0.010884 *
## `condition.fctrFor parts or not working` -3.984 7.41e-05 ***
## `condition.fctrManufacturer refurbished` -0.501 0.616843
## condition.fctrNew 5.381 9.81e-08 ***
## `condition.fctrNew other (see details)` 2.504 0.012487 *
## `condition.fctrSeller refurbished` -1.567 0.117623
## color.fctrBlack 0.670 0.503167
## color.fctrGold 0.514 0.607354
## `color.fctrSpace Gray` 1.935 0.053384 .
## color.fctrWhite 3.358 0.000824 ***
## D.ratio.nstopwrds.nwrds -0.027 0.978289
## D.TfIdf.sum.stem.stop.Ratio 0.475 0.635079
## `carrier.fctrAT&T` -0.093 0.925609
## carrier.fctrOther 0.564 0.572807
## carrier.fctrSprint -0.382 0.702420
## `carrier.fctrT-Mobile` -0.073 0.941729
## carrier.fctrUnknown -0.017 0.986123
## carrier.fctrVerizon -0.057 0.954947
## storage.fctr16 -6.760 2.72e-11 ***
## storage.fctr32 -5.893 5.65e-09 ***
## storage.fctr64 -4.227 2.66e-05 ***
## storage.fctrUnknown -3.668 0.000262 ***
## D.npnct14.log 0.377 0.706597
## D.terms.n.stem.stop.Ratio -0.101 0.919782
## cellular.fctr1 0.105 0.916038
## cellular.fctrUnknown -0.157 0.875103
## D.ndgts.log -0.348 0.728137
## .rnorm 0.020 0.984409
## idseq.my -1.628 0.103995
## D.npnct08.log 0.678 0.498236
## D.npnct05.log -1.287 0.198484
## D.npnct15.log -0.269 0.787897
## D.npnct01.log 1.236 0.216987
## D.npnct16.log -0.061 0.951397
## D.npnct12.log 0.355 0.722837
## D.npnct06.log 1.014 0.310882
## D.npnct03.log -0.117 0.906659
## D.nstopwrds.log -0.894 0.371675
## D.npnct11.log -1.934 0.053450 .
## D.npnct13.log -0.755 0.450219
## D.terms.n.post.stop -0.297 0.766458
## D.terms.n.post.stem 0.252 0.801338
## D.nwrds.log 2.324 0.020410 *
## D.terms.n.post.stop.log 0.035 0.971833
## D.nwrds.unq.log -0.065 0.947898
## D.terms.n.post.stem.log -0.065 0.947898
## D.nchrs.log -1.994 0.046552 *
## D.nuppr.log 2.010 0.044823 *
## D.npnct24.log -0.915 0.360703
## D.TfIdf.sum.post.stem -0.011 0.991140
## D.sum.TfIdf -0.011 0.991140
## D.TfIdf.sum.post.stop 0.154 0.877303
## D.ratio.sum.TfIdf.nwrds -0.330 0.741700
## biddable -18.658 < 2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 3.640 0.000291 ***
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.580 0.561891
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.208 0.227233
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.605 0.545574
## `prdline.my.fctriPadAir:.clusterid.fctr2` -2.014 0.044379 *
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.196 0.232057
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 2.100 0.036022 *
## `prdline.my.fctrUnknown:.clusterid.fctr3` 1.229 0.219446
## `prdline.my.fctriPad 1:.clusterid.fctr3` 1.463 0.143933
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.102 0.919095
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.069 0.944866
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.913 0.361427
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.654 0.513191
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.369 0.712353
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.744 0.456833
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.335 0.737895
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.018 0.309108
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.333 0.738874
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.119 0.905071
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 1.075 0.282688
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.249 0.803649
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000 1.000000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 7353.341)
##
## Null deviance: 14768530 on 859 degrees of freedom
## Residual deviance: 5676779 on 772 degrees of freedom
## AIC: 10182
##
## Number of Fisher Scoring iterations: 13
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.bayesglm bayesglm
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.318 0.385
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.6156165 95.49461 0.6037972 134.0346 10182.24
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.486531 2.262606 0.02327135
## label step_major step_minor bgn end elapsed
## 4 fit.models_1_bayesglm 4 0 122.662 126.127 3.465
## 5 fit.models_1_glmnet 5 0 126.128 NA NA
## [1] "fitting model: All.X.glmnet"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr"## Loading required package: glmnet
## Loaded glmnet 2.0-2
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 12.6 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 8700 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 87 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 1.723211e+02
## prdline.my.fctriPad 1
## -5.229110e+01
## prdline.my.fctriPad 2
## -2.035686e+01
## prdline.my.fctriPad 3+
## 4.722692e+00
## prdline.my.fctriPadAir
## 9.345764e+01
## prdline.my.fctriPadmini
## -6.956331e+00
## prdline.my.fctriPadmini 2+
## 3.810330e+01
## condition.fctrFor parts or not working
## -4.728636e+01
## condition.fctrNew
## 7.035378e+01
## condition.fctrNew other (see details)
## 3.737651e+01
## condition.fctrSeller refurbished
## -1.630047e+01
## color.fctrGold
## 1.186356e+01
## color.fctrSpace Gray
## 1.628852e+01
## color.fctrWhite
## 2.084031e+01
## D.TfIdf.sum.stem.stop.Ratio
## 6.985467e+01
## carrier.fctrAT&T
## -7.233273e-01
## carrier.fctrOther
## 6.178034e+01
## carrier.fctrSprint
## -2.897742e+01
## carrier.fctrVerizon
## 3.679945e+00
## storage.fctr16
## -4.263462e+01
## storage.fctr32
## -2.613513e+01
## storage.fctr64
## 2.567141e+00
## storage.fctrUnknown
## -5.036982e+00
## cellular.fctr1
## 5.761524e+00
## cellular.fctrUnknown
## -3.046442e+01
## idseq.my
## -6.630004e-03
## D.npnct08.log
## 3.994194e+00
## D.npnct05.log
## -4.846090e+01
## D.npnct15.log
## -6.406446e+00
## D.npnct16.log
## 1.979655e+01
## D.npnct06.log
## 1.239291e-01
## D.nstopwrds.log
## 6.463568e-01
## D.npnct11.log
## -9.085912e+00
## D.npnct13.log
## -2.437939e+00
## D.ratio.sum.TfIdf.nwrds
## -1.804864e+01
## biddable
## -1.220203e+02
## prdline.my.fctrUnknown:.clusterid.fctr2
## 4.980159e+01
## prdline.my.fctriPad 3+:.clusterid.fctr2
## 3.388813e-02
## prdline.my.fctriPadAir:.clusterid.fctr2
## -3.224287e+01
## prdline.my.fctriPadmini:.clusterid.fctr2
## 4.049513e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 5.943687e+01
## prdline.my.fctriPad 1:.clusterid.fctr3
## 1.063307e+00
## prdline.my.fctriPadAir:.clusterid.fctr3
## -3.629651e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -1.522672e+00
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 3.893825e+00
## prdline.my.fctriPad 2:.clusterid.fctr5
## 2.190215e+00
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 1.831389e+02
## prdline.my.fctriPad 1
## -5.403468e+01
## prdline.my.fctriPad 2
## -1.709190e+01
## prdline.my.fctriPad 3+
## 1.578141e+01
## prdline.my.fctriPadAir
## 1.147605e+02
## prdline.my.fctriPadmini
## -2.904453e-02
## prdline.my.fctriPadmini 2+
## 4.994664e+01
## condition.fctrFor parts or not working
## -4.858078e+01
## condition.fctrManufacturer refurbished
## -1.312452e+01
## condition.fctrNew
## 6.435301e+01
## condition.fctrNew other (see details)
## 4.125196e+01
## condition.fctrSeller refurbished
## -2.338919e+01
## color.fctrBlack
## 5.992460e+00
## color.fctrGold
## 1.251793e+01
## color.fctrSpace Gray
## 2.196532e+01
## color.fctrWhite
## 2.819827e+01
## D.ratio.nstopwrds.nwrds
## -2.119672e+01
## D.TfIdf.sum.stem.stop.Ratio
## 1.499879e+02
## carrier.fctrAT&T
## -7.246921e+00
## carrier.fctrOther
## 1.169892e+02
## carrier.fctrSprint
## -5.331061e+01
## carrier.fctrT-Mobile
## -4.232481e+00
## carrier.fctrUnknown
## 5.327543e+00
## storage.fctr16
## -1.322418e+02
## storage.fctr32
## -1.196622e+02
## storage.fctr64
## -8.392674e+01
## storage.fctrUnknown
## -9.601636e+01
## D.npnct14.log
## 7.365120e+00
## D.terms.n.stem.stop.Ratio
## 2.353523e+01
## cellular.fctr1
## 8.659197e+00
## cellular.fctrUnknown
## -3.644433e+01
## D.ndgts.log
## -1.116389e+01
## .rnorm
## -1.270692e-01
## idseq.my
## -1.161804e-02
## D.npnct08.log
## 1.080392e+01
## D.npnct05.log
## -1.075506e+02
## D.npnct15.log
## -2.098122e+01
## D.npnct01.log
## 9.637109e+00
## D.npnct16.log
## -9.723021e+00
## D.npnct12.log
## 8.549298e+00
## D.npnct06.log
## 6.246601e+01
## D.npnct03.log
## -9.776122e+00
## D.nstopwrds.log
## -2.093817e+01
## D.npnct11.log
## -2.932946e+01
## D.npnct13.log
## -1.895211e+01
## D.terms.n.post.stop
## -1.401095e+00
## D.nwrds.log
## 7.812375e+01
## D.terms.n.post.stop.log
## -1.997032e+00
## D.nwrds.unq.log
## -4.257706e-02
## D.terms.n.post.stem.log
## -8.959857e-04
## D.nchrs.log
## -8.063276e+00
## D.nuppr.log
## 8.840815e+00
## D.npnct24.log
## -2.395421e+02
## D.TfIdf.sum.post.stem
## -6.408130e-02
## D.sum.TfIdf
## -9.014118e-04
## D.TfIdf.sum.post.stop
## 8.031068e-01
## D.ratio.sum.TfIdf.nwrds
## -2.627968e+00
## biddable
## -1.382727e+02
## prdline.my.fctrUnknown:.clusterid.fctr2
## 9.046365e+01
## prdline.my.fctriPad 1:.clusterid.fctr2
## 1.851072e+01
## prdline.my.fctriPad 2:.clusterid.fctr2
## 2.733453e+01
## prdline.my.fctriPad 3+:.clusterid.fctr2
## 1.870096e+01
## prdline.my.fctriPadAir:.clusterid.fctr2
## -4.517034e+01
## prdline.my.fctriPadmini:.clusterid.fctr2
## 3.337833e+01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 7.051866e+01
## prdline.my.fctrUnknown:.clusterid.fctr3
## 5.565093e+01
## prdline.my.fctriPad 1:.clusterid.fctr3
## 4.202643e+01
## prdline.my.fctriPad 2:.clusterid.fctr3
## -1.400903e+00
## prdline.my.fctriPad 3+:.clusterid.fctr3
## -1.500819e+00
## prdline.my.fctriPadAir:.clusterid.fctr3
## -2.683633e+01
## prdline.my.fctriPadmini:.clusterid.fctr3
## 1.695523e+01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## 1.531007e+00
## prdline.my.fctriPad 1:.clusterid.fctr4
## 3.153175e+01
## prdline.my.fctriPad 2:.clusterid.fctr4
## 9.351827e+00
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 2.638727e+01
## prdline.my.fctriPadAir:.clusterid.fctr4
## -1.260653e+01
## prdline.my.fctriPadmini:.clusterid.fctr4
## -7.955986e+00
## prdline.my.fctriPad 2:.clusterid.fctr5
## 3.798240e+01
## prdline.my.fctriPadmini:.clusterid.fctr5
## 1.396481e+01
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.glmnet glmnet
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 9 1.591 0.047
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.583641 89.83738 0.5792309 138.1274 0.5330513
## min.RMSESD.fit max.RsquaredSD.fit
## 1 4.172042 0.04997399
## label step_major step_minor bgn end elapsed
## 5 fit.models_1_glmnet 5 0 126.128 129.559 3.431
## 6 fit.models_1_rpart 6 0 129.560 NA NA
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0803 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 860
##
## CP nsplit rel error
## 1 0.22941102 0 1.0000000
## 2 0.08271687 1 0.7705890
## 3 0.08034499 2 0.6878721
##
## Variable importance
## biddable
## 63
## prdline.my.fctriPadAir
## 23
## idseq.my
## 3
## color.fctrGold
## 3
## prdline.my.fctriPadAir:.clusterid.fctr2
## 2
## D.npnct15.log
## 1
## prdline.my.fctriPadAir:.clusterid.fctr3
## 1
## prdline.my.fctriPadAir:.clusterid.fctr4
## 1
## condition.fctrManufacturer refurbished
## 1
## D.TfIdf.sum.stem.stop.Ratio
## 1
## prdline.my.fctriPad 2:.clusterid.fctr5
## 1
##
## Node number 1: 860 observations, complexity param=0.229411
## mean=127.4371, MSE=17172.71
## left son=2 (640 obs) right son=3 (220 obs)
## Primary splits:
## biddable < 0.5 to the right, improve=0.22941100, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.14781390, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.13039270, (0 missing)
## condition.fctrFor parts or not working < 0.5 to the right, improve=0.05958729, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.05938979, (0 missing)
## Surrogate splits:
## idseq.my < 1783.5 to the left, agree=0.757, adj=0.050, (0 split)
## D.npnct15.log < 0.3465736 to the left, agree=0.750, adj=0.023, (0 split)
## D.TfIdf.sum.stem.stop.Ratio < 0.8214259 to the right, agree=0.747, adj=0.009, (0 split)
## prdline.my.fctriPad 2:.clusterid.fctr5 < 0.5 to the left, agree=0.747, adj=0.009, (0 split)
## D.npnct01.log < 1.242453 to the left, agree=0.745, adj=0.005, (0 split)
##
## Node number 2: 640 observations
## mean=90.63711, MSE=11139.65
##
## Node number 3: 220 observations, complexity param=0.08271687
## mean=234.4917, MSE=19323.14
## left son=6 (183 obs) right son=7 (37 obs)
## Primary splits:
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.28736310, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.18444820, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.17624070, (0 missing)
## condition.fctrFor parts or not working < 0.5 to the right, improve=0.13962800, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.09007232, (0 missing)
## Surrogate splits:
## color.fctrGold < 0.5 to the left, agree=0.855, adj=0.135, (0 split)
## prdline.my.fctriPadAir:.clusterid.fctr2 < 0.5 to the left, agree=0.845, adj=0.081, (0 split)
## prdline.my.fctriPadAir:.clusterid.fctr3 < 0.5 to the left, agree=0.841, adj=0.054, (0 split)
## prdline.my.fctriPadAir:.clusterid.fctr4 < 0.5 to the left, agree=0.841, adj=0.054, (0 split)
## condition.fctrManufacturer refurbished < 0.5 to the left, agree=0.836, adj=0.027, (0 split)
##
## Node number 6: 183 observations
## mean=200.9851, MSE=13424.58
##
## Node number 7: 37 observations
## mean=400.2132, MSE=15480.72
##
## n= 860
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 860 14768530.0 127.43710
## 2) biddable>=0.5 640 7129375.0 90.63711 *
## 3) biddable< 0.5 220 4251091.0 234.49170
## 6) prdline.my.fctriPadAir< 0.5 183 2456698.0 200.98510 *
## 7) prdline.my.fctriPadAir>=0.5 37 572786.8 400.21320 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.441 0.059
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.3121279 111.8385 0.450545 157.8425 0.2750573
## min.RMSESD.fit max.RsquaredSD.fit
## 1 3.592112 0.04148092
## label step_major step_minor bgn end elapsed
## 6 fit.models_1_rpart 6 0 129.560 133.2 3.64
## 7 fit.models_1_rf 7 0 133.201 NA NA
## [1] "fitting model: All.X.no.rnorm.rf"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
##
## The following object is masked from 'package:gdata':
##
## combine
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 44 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 860 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 860 -none- numeric
## importance 86 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 860 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 86 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.no.rnorm.rf rf
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 24.469 8.461
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.8931938 91.81702 0.6249482 130.2892 0.5195214
## min.RMSESD.fit max.RsquaredSD.fit
## 1 5.81286 0.055536
## label step_major step_minor bgn end elapsed
## 7 fit.models_1_rf 7 0 133.201 160.089 26.888
## 8 fit.models_1_lm 8 0 160.089 NA NA
## [1] "fitting model: All.Interact.X.lm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -282.06 -41.46 -2.59 37.51 345.84
##
## Coefficients: (10 not defined because of singularities)
## Estimate Std. Error
## (Intercept) -7.838e+04 8.155e+04
## `prdline.my.fctriPad 1` -7.177e+01 4.047e+01
## `prdline.my.fctriPad 2` 1.025e+00 3.955e+01
## `prdline.my.fctriPad 3+` 7.416e+01 3.828e+01
## prdline.my.fctriPadAir 2.637e+02 3.886e+01
## prdline.my.fctriPadmini 2.430e+01 3.869e+01
## `prdline.my.fctriPadmini 2+` 1.939e+02 4.453e+01
## `condition.fctrFor parts or not working` -6.006e+01 1.239e+01
## `condition.fctrManufacturer refurbished` -2.978e+01 2.342e+01
## condition.fctrNew 6.237e+01 1.160e+01
## `condition.fctrNew other (see details)` 5.125e+01 1.627e+01
## `condition.fctrSeller refurbished` -1.878e+01 1.588e+01
## color.fctrBlack 4.703e+00 8.140e+00
## color.fctrGold 6.338e+00 2.096e+01
## `color.fctrSpace Gray` 1.768e+01 1.079e+01
## color.fctrWhite 2.584e+01 8.111e+00
## D.ratio.nstopwrds.nwrds -1.488e+01 2.442e+02
## D.TfIdf.sum.stem.stop.Ratio 1.899e+02 5.759e+02
## `carrier.fctrAT&T` -4.036e+01 2.332e+01
## carrier.fctrOther 7.801e+01 5.753e+01
## carrier.fctrSprint -9.735e+01 3.206e+01
## `carrier.fctrT-Mobile` -2.903e+01 3.729e+01
## carrier.fctrUnknown -2.723e+01 1.723e+01
## carrier.fctrVerizon -3.173e+01 2.398e+01
## storage.fctr16 -1.294e+02 1.917e+01
## storage.fctr32 -1.186e+02 2.006e+01
## storage.fctr64 -8.355e+01 1.979e+01
## storage.fctrUnknown -1.044e+02 2.597e+01
## D.npnct14.log 2.902e+00 3.779e+01
## D.terms.n.stem.stop.Ratio 7.847e+04 8.159e+04
## cellular.fctr1 4.561e+01 2.114e+01
## cellular.fctrUnknown NA NA
## D.ndgts.log 4.255e+00 1.862e+01
## .rnorm 1.640e+00 2.955e+00
## D.npnct08.log 1.711e+01 2.318e+01
## D.npnct05.log -8.928e+01 7.143e+01
## D.npnct15.log 1.909e+01 3.273e+01
## D.npnct01.log 2.390e+01 2.237e+01
## D.npnct16.log -5.978e+00 6.473e+01
## D.npnct12.log 6.040e+00 2.406e+01
## D.npnct06.log 7.244e+01 7.570e+01
## D.npnct03.log -3.384e+01 5.129e+01
## D.nstopwrds.log -4.957e+01 7.397e+01
## D.npnct11.log -2.491e+01 1.269e+01
## D.npnct13.log -8.729e+00 1.302e+01
## D.terms.n.post.stop -5.738e+02 5.594e+02
## D.terms.n.post.stem 5.731e+02 5.609e+02
## D.nwrds.log 1.758e+02 9.127e+01
## D.terms.n.post.stop.log 8.826e+04 9.139e+04
## D.nwrds.unq.log -8.832e+04 9.141e+04
## D.terms.n.post.stem.log NA NA
## D.nchrs.log -3.340e+02 1.664e+02
## D.nuppr.log 3.086e+02 1.456e+02
## D.npnct24.log -1.285e+02 2.082e+02
## D.TfIdf.sum.post.stem -1.955e+01 9.121e+01
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 2.094e+01 8.721e+01
## D.ratio.sum.TfIdf.nwrds -1.033e+01 1.736e+01
## biddable -1.134e+02 2.277e+01
## idseq.my 3.313e-02 2.025e-02
## `prdline.my.fctriPad 1:biddable` 5.737e+01 3.006e+01
## `prdline.my.fctriPad 2:biddable` 4.229e+00 2.904e+01
## `prdline.my.fctriPad 3+:biddable` -2.412e+01 2.841e+01
## `prdline.my.fctriPadAir:biddable` -1.022e+02 2.813e+01
## `prdline.my.fctriPadmini:biddable` 3.423e+00 2.834e+01
## `prdline.my.fctriPadmini 2+:biddable` -8.363e+01 3.125e+01
## `prdline.my.fctriPad 1:idseq.my` -1.999e-02 2.450e-02
## `prdline.my.fctriPad 2:idseq.my` -1.714e-02 2.384e-02
## `prdline.my.fctriPad 3+:idseq.my` -4.368e-02 2.308e-02
## `prdline.my.fctriPadAir:idseq.my` -8.240e-02 2.414e-02
## `prdline.my.fctriPadmini:idseq.my` -2.687e-02 2.328e-02
## `prdline.my.fctriPadmini 2+:idseq.my` -9.478e-02 2.893e-02
## `prdline.my.fctrUnknown:.clusterid.fctr2` 9.445e+01 2.560e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.522e+01 2.566e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 2.937e+01 2.118e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 1.806e+01 2.774e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -3.691e+01 2.238e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 3.780e+01 2.760e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 7.011e+01 3.227e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` 4.600e+01 4.175e+01
## `prdline.my.fctriPad 1:.clusterid.fctr3` 2.990e+01 2.819e+01
## `prdline.my.fctriPad 2:.clusterid.fctr3` 7.565e+00 4.231e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -5.099e-01 2.511e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -2.297e+01 3.210e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.777e+01 2.488e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 1.149e+01 3.358e+01
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 1.468e+01 3.466e+01
## `prdline.my.fctriPad 2:.clusterid.fctr4` 8.345e+00 3.017e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 2.484e+01 2.620e+01
## `prdline.my.fctriPadAir:.clusterid.fctr4` -2.728e+00 3.390e+01
## `prdline.my.fctriPadmini:.clusterid.fctr4` 1.458e+00 3.206e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 4.645e+01 3.477e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.743e+01 4.602e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## t value Pr(>|t|)
## (Intercept) -0.961 0.336844
## `prdline.my.fctriPad 1` -1.773 0.076553 .
## `prdline.my.fctriPad 2` 0.026 0.979320
## `prdline.my.fctriPad 3+` 1.937 0.053073 .
## prdline.my.fctriPadAir 6.787 2.29e-11 ***
## prdline.my.fctriPadmini 0.628 0.530190
## `prdline.my.fctriPadmini 2+` 4.356 1.51e-05 ***
## `condition.fctrFor parts or not working` -4.849 1.50e-06 ***
## `condition.fctrManufacturer refurbished` -1.272 0.203854
## condition.fctrNew 5.375 1.01e-07 ***
## `condition.fctrNew other (see details)` 3.149 0.001701 **
## `condition.fctrSeller refurbished` -1.183 0.237361
## color.fctrBlack 0.578 0.563639
## color.fctrGold 0.302 0.762413
## `color.fctrSpace Gray` 1.639 0.101673
## color.fctrWhite 3.185 0.001505 **
## D.ratio.nstopwrds.nwrds -0.061 0.951417
## D.TfIdf.sum.stem.stop.Ratio 0.330 0.741672
## `carrier.fctrAT&T` -1.731 0.083884 .
## carrier.fctrOther 1.356 0.175536
## carrier.fctrSprint -3.037 0.002473 **
## `carrier.fctrT-Mobile` -0.779 0.436505
## carrier.fctrUnknown -1.580 0.114488
## carrier.fctrVerizon -1.323 0.186295
## storage.fctr16 -6.747 2.96e-11 ***
## storage.fctr32 -5.912 5.07e-09 ***
## storage.fctr64 -4.221 2.72e-05 ***
## storage.fctrUnknown -4.020 6.40e-05 ***
## D.npnct14.log 0.077 0.938798
## D.terms.n.stem.stop.Ratio 0.962 0.336450
## cellular.fctr1 2.157 0.031318 *
## cellular.fctrUnknown NA NA
## D.ndgts.log 0.229 0.819286
## .rnorm 0.555 0.579066
## D.npnct08.log 0.738 0.460838
## D.npnct05.log -1.250 0.211717
## D.npnct15.log 0.583 0.559986
## D.npnct01.log 1.068 0.285666
## D.npnct16.log -0.092 0.926450
## D.npnct12.log 0.251 0.801801
## D.npnct06.log 0.957 0.338905
## D.npnct03.log -0.660 0.509648
## D.nstopwrds.log -0.670 0.502972
## D.npnct11.log -1.962 0.050118 .
## D.npnct13.log -0.671 0.502716
## D.terms.n.post.stop -1.026 0.305278
## D.terms.n.post.stem 1.022 0.307172
## D.nwrds.log 1.926 0.054447 .
## D.terms.n.post.stop.log 0.966 0.334484
## D.nwrds.unq.log -0.966 0.334238
## D.terms.n.post.stem.log NA NA
## D.nchrs.log -2.007 0.045083 *
## D.nuppr.log 2.119 0.034430 *
## D.npnct24.log -0.617 0.537260
## D.TfIdf.sum.post.stem -0.214 0.830309
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 0.240 0.810273
## D.ratio.sum.TfIdf.nwrds -0.595 0.552123
## biddable -4.981 7.80e-07 ***
## idseq.my 1.636 0.102172
## `prdline.my.fctriPad 1:biddable` 1.909 0.056667 .
## `prdline.my.fctriPad 2:biddable` 0.146 0.884229
## `prdline.my.fctriPad 3+:biddable` -0.849 0.396167
## `prdline.my.fctriPadAir:biddable` -3.632 0.000300 ***
## `prdline.my.fctriPadmini:biddable` 0.121 0.903876
## `prdline.my.fctriPadmini 2+:biddable` -2.676 0.007612 **
## `prdline.my.fctriPad 1:idseq.my` -0.816 0.414873
## `prdline.my.fctriPad 2:idseq.my` -0.719 0.472385
## `prdline.my.fctriPad 3+:idseq.my` -1.892 0.058803 .
## `prdline.my.fctriPadAir:idseq.my` -3.414 0.000674 ***
## `prdline.my.fctriPadmini:idseq.my` -1.154 0.248725
## `prdline.my.fctriPadmini 2+:idseq.my` -3.277 0.001097 **
## `prdline.my.fctrUnknown:.clusterid.fctr2` 3.690 0.000240 ***
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.593 0.553391
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.387 0.165799
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.651 0.515087
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.649 0.099511 .
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.369 0.171316
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 2.173 0.030122 *
## `prdline.my.fctrUnknown:.clusterid.fctr3` 1.102 0.270853
## `prdline.my.fctriPad 1:.clusterid.fctr3` 1.061 0.289168
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.179 0.858153
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.020 0.983801
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.716 0.474512
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.714 0.475412
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.342 0.732300
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.423 0.672111
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.277 0.782139
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.948 0.343472
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.080 0.935868
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.045 0.963748
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 1.336 0.181936
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.379 0.704911
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 82.31 on 770 degrees of freedom
## Multiple R-squared: 0.6468, Adjusted R-squared: 0.6059
## F-statistic: 15.84 on 89 and 770 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
## model_id model_method
## 1 All.Interact.X.lm lm
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.156 0.045
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.6467622 94.75209 0.6027366 134.2138 0.6059334
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.5019222 2.819659 0.01481398
## label step_major step_minor bgn end elapsed
## 8 fit.models_1_lm 8 0 160.089 163.087 2.998
## 9 fit.models_1_glm 9 0 163.088 NA NA
## [1] "fitting model: All.Interact.X.glm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -282.06 -41.46 -2.59 37.51 345.84
##
## Coefficients: (10 not defined because of singularities)
## Estimate Std. Error
## (Intercept) -7.838e+04 8.155e+04
## `prdline.my.fctriPad 1` -7.177e+01 4.047e+01
## `prdline.my.fctriPad 2` 1.025e+00 3.955e+01
## `prdline.my.fctriPad 3+` 7.416e+01 3.828e+01
## prdline.my.fctriPadAir 2.637e+02 3.886e+01
## prdline.my.fctriPadmini 2.430e+01 3.869e+01
## `prdline.my.fctriPadmini 2+` 1.939e+02 4.453e+01
## `condition.fctrFor parts or not working` -6.006e+01 1.239e+01
## `condition.fctrManufacturer refurbished` -2.978e+01 2.342e+01
## condition.fctrNew 6.237e+01 1.160e+01
## `condition.fctrNew other (see details)` 5.125e+01 1.627e+01
## `condition.fctrSeller refurbished` -1.878e+01 1.588e+01
## color.fctrBlack 4.703e+00 8.140e+00
## color.fctrGold 6.338e+00 2.096e+01
## `color.fctrSpace Gray` 1.768e+01 1.079e+01
## color.fctrWhite 2.584e+01 8.111e+00
## D.ratio.nstopwrds.nwrds -1.488e+01 2.442e+02
## D.TfIdf.sum.stem.stop.Ratio 1.899e+02 5.759e+02
## `carrier.fctrAT&T` -4.036e+01 2.332e+01
## carrier.fctrOther 7.801e+01 5.753e+01
## carrier.fctrSprint -9.735e+01 3.206e+01
## `carrier.fctrT-Mobile` -2.903e+01 3.729e+01
## carrier.fctrUnknown -2.723e+01 1.723e+01
## carrier.fctrVerizon -3.173e+01 2.398e+01
## storage.fctr16 -1.294e+02 1.917e+01
## storage.fctr32 -1.186e+02 2.006e+01
## storage.fctr64 -8.355e+01 1.979e+01
## storage.fctrUnknown -1.044e+02 2.597e+01
## D.npnct14.log 2.902e+00 3.779e+01
## D.terms.n.stem.stop.Ratio 7.847e+04 8.159e+04
## cellular.fctr1 4.561e+01 2.114e+01
## cellular.fctrUnknown NA NA
## D.ndgts.log 4.255e+00 1.862e+01
## .rnorm 1.640e+00 2.955e+00
## D.npnct08.log 1.711e+01 2.318e+01
## D.npnct05.log -8.928e+01 7.143e+01
## D.npnct15.log 1.909e+01 3.273e+01
## D.npnct01.log 2.390e+01 2.237e+01
## D.npnct16.log -5.978e+00 6.473e+01
## D.npnct12.log 6.040e+00 2.406e+01
## D.npnct06.log 7.244e+01 7.570e+01
## D.npnct03.log -3.384e+01 5.129e+01
## D.nstopwrds.log -4.957e+01 7.397e+01
## D.npnct11.log -2.491e+01 1.269e+01
## D.npnct13.log -8.729e+00 1.302e+01
## D.terms.n.post.stop -5.738e+02 5.594e+02
## D.terms.n.post.stem 5.731e+02 5.609e+02
## D.nwrds.log 1.758e+02 9.127e+01
## D.terms.n.post.stop.log 8.826e+04 9.139e+04
## D.nwrds.unq.log -8.832e+04 9.141e+04
## D.terms.n.post.stem.log NA NA
## D.nchrs.log -3.340e+02 1.664e+02
## D.nuppr.log 3.086e+02 1.456e+02
## D.npnct24.log -1.285e+02 2.082e+02
## D.TfIdf.sum.post.stem -1.955e+01 9.121e+01
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 2.094e+01 8.721e+01
## D.ratio.sum.TfIdf.nwrds -1.033e+01 1.736e+01
## biddable -1.134e+02 2.277e+01
## idseq.my 3.313e-02 2.025e-02
## `prdline.my.fctriPad 1:biddable` 5.737e+01 3.006e+01
## `prdline.my.fctriPad 2:biddable` 4.229e+00 2.904e+01
## `prdline.my.fctriPad 3+:biddable` -2.412e+01 2.841e+01
## `prdline.my.fctriPadAir:biddable` -1.022e+02 2.813e+01
## `prdline.my.fctriPadmini:biddable` 3.423e+00 2.834e+01
## `prdline.my.fctriPadmini 2+:biddable` -8.363e+01 3.125e+01
## `prdline.my.fctriPad 1:idseq.my` -1.999e-02 2.450e-02
## `prdline.my.fctriPad 2:idseq.my` -1.714e-02 2.384e-02
## `prdline.my.fctriPad 3+:idseq.my` -4.368e-02 2.308e-02
## `prdline.my.fctriPadAir:idseq.my` -8.240e-02 2.414e-02
## `prdline.my.fctriPadmini:idseq.my` -2.687e-02 2.328e-02
## `prdline.my.fctriPadmini 2+:idseq.my` -9.478e-02 2.893e-02
## `prdline.my.fctrUnknown:.clusterid.fctr2` 9.445e+01 2.560e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.522e+01 2.566e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 2.937e+01 2.118e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 1.806e+01 2.774e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -3.691e+01 2.238e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 3.780e+01 2.760e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 7.011e+01 3.227e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` 4.600e+01 4.175e+01
## `prdline.my.fctriPad 1:.clusterid.fctr3` 2.990e+01 2.819e+01
## `prdline.my.fctriPad 2:.clusterid.fctr3` 7.565e+00 4.231e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -5.099e-01 2.511e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -2.297e+01 3.210e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.777e+01 2.488e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 1.149e+01 3.358e+01
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 1.468e+01 3.466e+01
## `prdline.my.fctriPad 2:.clusterid.fctr4` 8.345e+00 3.017e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 2.484e+01 2.620e+01
## `prdline.my.fctriPadAir:.clusterid.fctr4` -2.728e+00 3.390e+01
## `prdline.my.fctriPadmini:.clusterid.fctr4` 1.458e+00 3.206e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 4.645e+01 3.477e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.743e+01 4.602e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## t value Pr(>|t|)
## (Intercept) -0.961 0.336844
## `prdline.my.fctriPad 1` -1.773 0.076553 .
## `prdline.my.fctriPad 2` 0.026 0.979320
## `prdline.my.fctriPad 3+` 1.937 0.053073 .
## prdline.my.fctriPadAir 6.787 2.29e-11 ***
## prdline.my.fctriPadmini 0.628 0.530190
## `prdline.my.fctriPadmini 2+` 4.356 1.51e-05 ***
## `condition.fctrFor parts or not working` -4.849 1.50e-06 ***
## `condition.fctrManufacturer refurbished` -1.272 0.203854
## condition.fctrNew 5.375 1.01e-07 ***
## `condition.fctrNew other (see details)` 3.149 0.001701 **
## `condition.fctrSeller refurbished` -1.183 0.237361
## color.fctrBlack 0.578 0.563639
## color.fctrGold 0.302 0.762413
## `color.fctrSpace Gray` 1.639 0.101673
## color.fctrWhite 3.185 0.001505 **
## D.ratio.nstopwrds.nwrds -0.061 0.951417
## D.TfIdf.sum.stem.stop.Ratio 0.330 0.741672
## `carrier.fctrAT&T` -1.731 0.083884 .
## carrier.fctrOther 1.356 0.175536
## carrier.fctrSprint -3.037 0.002473 **
## `carrier.fctrT-Mobile` -0.779 0.436505
## carrier.fctrUnknown -1.580 0.114488
## carrier.fctrVerizon -1.323 0.186295
## storage.fctr16 -6.747 2.96e-11 ***
## storage.fctr32 -5.912 5.07e-09 ***
## storage.fctr64 -4.221 2.72e-05 ***
## storage.fctrUnknown -4.020 6.40e-05 ***
## D.npnct14.log 0.077 0.938798
## D.terms.n.stem.stop.Ratio 0.962 0.336450
## cellular.fctr1 2.157 0.031318 *
## cellular.fctrUnknown NA NA
## D.ndgts.log 0.229 0.819286
## .rnorm 0.555 0.579066
## D.npnct08.log 0.738 0.460838
## D.npnct05.log -1.250 0.211717
## D.npnct15.log 0.583 0.559986
## D.npnct01.log 1.068 0.285666
## D.npnct16.log -0.092 0.926450
## D.npnct12.log 0.251 0.801801
## D.npnct06.log 0.957 0.338905
## D.npnct03.log -0.660 0.509648
## D.nstopwrds.log -0.670 0.502972
## D.npnct11.log -1.962 0.050118 .
## D.npnct13.log -0.671 0.502716
## D.terms.n.post.stop -1.026 0.305278
## D.terms.n.post.stem 1.022 0.307172
## D.nwrds.log 1.926 0.054447 .
## D.terms.n.post.stop.log 0.966 0.334484
## D.nwrds.unq.log -0.966 0.334238
## D.terms.n.post.stem.log NA NA
## D.nchrs.log -2.007 0.045083 *
## D.nuppr.log 2.119 0.034430 *
## D.npnct24.log -0.617 0.537260
## D.TfIdf.sum.post.stem -0.214 0.830309
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 0.240 0.810273
## D.ratio.sum.TfIdf.nwrds -0.595 0.552123
## biddable -4.981 7.80e-07 ***
## idseq.my 1.636 0.102172
## `prdline.my.fctriPad 1:biddable` 1.909 0.056667 .
## `prdline.my.fctriPad 2:biddable` 0.146 0.884229
## `prdline.my.fctriPad 3+:biddable` -0.849 0.396167
## `prdline.my.fctriPadAir:biddable` -3.632 0.000300 ***
## `prdline.my.fctriPadmini:biddable` 0.121 0.903876
## `prdline.my.fctriPadmini 2+:biddable` -2.676 0.007612 **
## `prdline.my.fctriPad 1:idseq.my` -0.816 0.414873
## `prdline.my.fctriPad 2:idseq.my` -0.719 0.472385
## `prdline.my.fctriPad 3+:idseq.my` -1.892 0.058803 .
## `prdline.my.fctriPadAir:idseq.my` -3.414 0.000674 ***
## `prdline.my.fctriPadmini:idseq.my` -1.154 0.248725
## `prdline.my.fctriPadmini 2+:idseq.my` -3.277 0.001097 **
## `prdline.my.fctrUnknown:.clusterid.fctr2` 3.690 0.000240 ***
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.593 0.553391
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.387 0.165799
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.651 0.515087
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.649 0.099511 .
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.369 0.171316
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 2.173 0.030122 *
## `prdline.my.fctrUnknown:.clusterid.fctr3` 1.102 0.270853
## `prdline.my.fctriPad 1:.clusterid.fctr3` 1.061 0.289168
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.179 0.858153
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.020 0.983801
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.716 0.474512
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.714 0.475412
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.342 0.732300
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.423 0.672111
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.277 0.782139
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.948 0.343472
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.080 0.935868
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.045 0.963748
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 1.336 0.181936
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.379 0.704911
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 6775.069)
##
## Null deviance: 14768530 on 859 degrees of freedom
## Residual deviance: 5216803 on 770 degrees of freedom
## AIC: 10114
##
## Number of Fisher Scoring iterations: 2
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## model_id model_method
## 1 All.Interact.X.glm glm
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.198 0.064
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.6467622 94.75209 0.6027366 134.2138 10113.57
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.5019222 2.819659 0.01481398
## label step_major step_minor bgn end elapsed
## 9 fit.models_1_glm 9 0 163.088 166.158 3.07
## 10 fit.models_1_bayesglm 10 0 166.159 NA NA
## [1] "fitting model: All.Interact.X.bayesglm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -282.69 -42.27 -3.09 38.39 344.21
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 248.76163 675.63975
## `prdline.my.fctriPad 1` -73.86164 40.10625
## `prdline.my.fctriPad 2` -2.66085 39.19853
## `prdline.my.fctriPad 3+` 73.69216 37.81801
## prdline.my.fctriPadAir 261.33006 38.51323
## prdline.my.fctriPadmini 23.01766 38.35046
## `prdline.my.fctriPadmini 2+` 190.58040 44.21473
## `condition.fctrFor parts or not working` -60.74167 12.42667
## `condition.fctrManufacturer refurbished` -29.56218 23.54083
## condition.fctrNew 62.14030 11.67505
## `condition.fctrNew other (see details)` 50.45921 16.35917
## `condition.fctrSeller refurbished` -19.98531 15.73918
## color.fctrBlack 4.91615 8.18051
## color.fctrGold 6.96191 21.05490
## `color.fctrSpace Gray` 18.34772 10.82209
## color.fctrWhite 26.01564 8.12050
## D.ratio.nstopwrds.nwrds 18.92750 212.91685
## D.TfIdf.sum.stem.stop.Ratio 96.10785 393.97456
## `carrier.fctrAT&T` -15.66391 171.68360
## carrier.fctrOther 101.84913 177.56998
## carrier.fctrSprint -72.01563 172.75483
## `carrier.fctrT-Mobile` -5.23567 173.37415
## carrier.fctrUnknown -2.87240 171.75339
## carrier.fctrVerizon -7.82249 171.76586
## storage.fctr16 -128.44124 19.23485
## storage.fctr32 -117.48660 20.12617
## storage.fctr64 -82.53111 19.86636
## storage.fctrUnknown -103.36064 26.05440
## D.npnct14.log 3.55051 37.74450
## D.terms.n.stem.stop.Ratio -88.99166 508.22307
## cellular.fctr1 21.03860 171.51392
## cellular.fctrUnknown -24.62878 172.39024
## D.ndgts.log 4.19630 18.30083
## .rnorm 1.47147 2.97066
## D.npnct08.log 15.66813 23.25524
## D.npnct05.log -90.15560 71.00009
## D.npnct15.log 17.47858 32.73283
## D.npnct01.log 22.32727 22.10254
## D.npnct16.log -5.96297 63.75590
## D.npnct12.log 5.74949 23.98421
## D.npnct06.log 69.87007 74.36206
## D.npnct03.log -35.33597 50.98749
## D.nstopwrds.log -53.61915 66.10934
## D.npnct11.log -26.03188 12.64661
## D.npnct13.log -10.23457 12.86488
## D.terms.n.post.stop -21.17595 65.80405
## D.terms.n.post.stem 19.73845 66.67100
## D.nwrds.log 162.19846 86.49883
## D.terms.n.post.stop.log 34.63771 430.71529
## D.nwrds.unq.log -40.21710 456.74618
## D.terms.n.post.stem.log -40.21710 456.74618
## D.nchrs.log -287.50214 152.39747
## D.nuppr.log 269.43209 134.10622
## D.npnct24.log -136.48215 186.33264
## D.TfIdf.sum.post.stem -2.88174 383.51494
## D.sum.TfIdf -2.88174 383.51494
## D.TfIdf.sum.post.stop 7.41312 60.85242
## D.ratio.sum.TfIdf.nwrds -9.51995 16.52385
## biddable -115.23284 22.56073
## idseq.my 0.03332 0.02017
## `prdline.my.fctriPad 1:biddable` 59.19721 29.89915
## `prdline.my.fctriPad 2:biddable` 5.65233 28.89493
## `prdline.my.fctriPad 3+:biddable` -22.89095 28.22171
## `prdline.my.fctriPadAir:biddable` -99.93963 27.96745
## `prdline.my.fctriPadmini:biddable` 4.89075 28.20803
## `prdline.my.fctriPadmini 2+:biddable` -81.14510 31.09539
## `prdline.my.fctriPad 1:idseq.my` -0.01985 0.02449
## `prdline.my.fctriPad 2:idseq.my` -0.01501 0.02382
## `prdline.my.fctriPad 3+:idseq.my` -0.04411 0.02300
## `prdline.my.fctriPadAir:idseq.my` -0.08231 0.02410
## `prdline.my.fctriPadmini:idseq.my` -0.02719 0.02323
## `prdline.my.fctriPadmini 2+:idseq.my` -0.09382 0.02892
## `prdline.my.fctrUnknown:.clusterid.fctr2` 94.31034 25.48436
## `prdline.my.fctriPad 1:.clusterid.fctr2` 15.64062 25.75248
## `prdline.my.fctriPad 2:.clusterid.fctr2` 29.57329 20.98194
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 17.74233 27.80795
## `prdline.my.fctriPadAir:.clusterid.fctr2` -36.76229 22.44753
## `prdline.my.fctriPadmini:.clusterid.fctr2` 34.66544 27.51666
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 72.90020 31.95080
## `prdline.my.fctrUnknown:.clusterid.fctr3` 45.96382 41.72406
## `prdline.my.fctriPad 1:.clusterid.fctr3` 30.16363 28.22372
## `prdline.my.fctriPad 2:.clusterid.fctr3` 9.25809 42.26131
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -1.31184 25.19735
## `prdline.my.fctriPadAir:.clusterid.fctr3` -22.03987 32.18598
## `prdline.my.fctriPadmini:.clusterid.fctr3` 16.70908 24.95584
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 8.88248 33.50712
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.00000 661.20057
## `prdline.my.fctriPad 1:.clusterid.fctr4` 14.65490 34.71998
## `prdline.my.fctriPad 2:.clusterid.fctr4` 8.13447 30.16712
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 24.67787 26.09690
## `prdline.my.fctriPadAir:.clusterid.fctr4` -2.48013 33.84541
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.98678 32.12987
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.00000 661.20057
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.00000 661.20057
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.00000 661.20057
## `prdline.my.fctriPad 2:.clusterid.fctr5` 46.99954 34.80381
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.00000 661.20057
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.00000 661.20057
## `prdline.my.fctriPadmini:.clusterid.fctr5` 16.07235 45.99696
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.00000 661.20057
## t value Pr(>|t|)
## (Intercept) 0.368 0.712836
## `prdline.my.fctriPad 1` -1.842 0.065916 .
## `prdline.my.fctriPad 2` -0.068 0.945898
## `prdline.my.fctriPad 3+` 1.949 0.051711 .
## prdline.my.fctriPadAir 6.785 2.33e-11 ***
## prdline.my.fctriPadmini 0.600 0.548557
## `prdline.my.fctriPadmini 2+` 4.310 1.84e-05 ***
## `condition.fctrFor parts or not working` -4.888 1.24e-06 ***
## `condition.fctrManufacturer refurbished` -1.256 0.209580
## condition.fctrNew 5.322 1.35e-07 ***
## `condition.fctrNew other (see details)` 3.084 0.002113 **
## `condition.fctrSeller refurbished` -1.270 0.204551
## color.fctrBlack 0.601 0.548046
## color.fctrGold 0.331 0.740996
## `color.fctrSpace Gray` 1.695 0.090410 .
## color.fctrWhite 3.204 0.001413 **
## D.ratio.nstopwrds.nwrds 0.089 0.929188
## D.TfIdf.sum.stem.stop.Ratio 0.244 0.807340
## `carrier.fctrAT&T` -0.091 0.927328
## carrier.fctrOther 0.574 0.566427
## carrier.fctrSprint -0.417 0.676894
## `carrier.fctrT-Mobile` -0.030 0.975917
## carrier.fctrUnknown -0.017 0.986661
## carrier.fctrVerizon -0.046 0.963688
## storage.fctr16 -6.678 4.69e-11 ***
## storage.fctr32 -5.838 7.85e-09 ***
## storage.fctr64 -4.154 3.63e-05 ***
## storage.fctrUnknown -3.967 7.96e-05 ***
## D.npnct14.log 0.094 0.925081
## D.terms.n.stem.stop.Ratio -0.175 0.861045
## cellular.fctr1 0.123 0.902406
## cellular.fctrUnknown -0.143 0.886434
## D.ndgts.log 0.229 0.818701
## .rnorm 0.495 0.620506
## D.npnct08.log 0.674 0.500678
## D.npnct05.log -1.270 0.204546
## D.npnct15.log 0.534 0.593513
## D.npnct01.log 1.010 0.312737
## D.npnct16.log -0.094 0.925509
## D.npnct12.log 0.240 0.810612
## D.npnct06.log 0.940 0.347725
## D.npnct03.log -0.693 0.488501
## D.nstopwrds.log -0.811 0.417581
## D.npnct11.log -2.058 0.039891 *
## D.npnct13.log -0.796 0.426546
## D.terms.n.post.stop -0.322 0.747690
## D.terms.n.post.stem 0.296 0.767267
## D.nwrds.log 1.875 0.061155 .
## D.terms.n.post.stop.log 0.080 0.935925
## D.nwrds.unq.log -0.088 0.929859
## D.terms.n.post.stem.log -0.088 0.929859
## D.nchrs.log -1.887 0.059605 .
## D.nuppr.log 2.009 0.044880 *
## D.npnct24.log -0.732 0.464111
## D.TfIdf.sum.post.stem -0.008 0.994007
## D.sum.TfIdf -0.008 0.994007
## D.TfIdf.sum.post.stop 0.122 0.903073
## D.ratio.sum.TfIdf.nwrds -0.576 0.564695
## biddable -5.108 4.13e-07 ***
## idseq.my 1.652 0.098934 .
## `prdline.my.fctriPad 1:biddable` 1.980 0.048076 *
## `prdline.my.fctriPad 2:biddable` 0.196 0.844962
## `prdline.my.fctriPad 3+:biddable` -0.811 0.417556
## `prdline.my.fctriPadAir:biddable` -3.573 0.000375 ***
## `prdline.my.fctriPadmini:biddable` 0.173 0.862398
## `prdline.my.fctriPadmini 2+:biddable` -2.610 0.009244 **
## `prdline.my.fctriPad 1:idseq.my` -0.811 0.417756
## `prdline.my.fctriPad 2:idseq.my` -0.630 0.528863
## `prdline.my.fctriPad 3+:idseq.my` -1.918 0.055534 .
## `prdline.my.fctriPadAir:idseq.my` -3.416 0.000670 ***
## `prdline.my.fctriPadmini:idseq.my` -1.170 0.242222
## `prdline.my.fctriPadmini 2+:idseq.my` -3.244 0.001230 **
## `prdline.my.fctrUnknown:.clusterid.fctr2` 3.701 0.000231 ***
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.607 0.543804
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.409 0.159107
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.638 0.523646
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.638 0.101899
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.260 0.208129
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 2.282 0.022786 *
## `prdline.my.fctrUnknown:.clusterid.fctr3` 1.102 0.270978
## `prdline.my.fctriPad 1:.clusterid.fctr3` 1.069 0.285529
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.219 0.826656
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.052 0.958492
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.685 0.493700
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.670 0.503350
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.265 0.791010
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.422 0.673080
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.270 0.787505
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.946 0.344640
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.073 0.941604
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.031 0.975507
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 1.350 0.177285
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.349 0.726869
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000 1.000000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 6874.293)
##
## Null deviance: 14768530 on 859 degrees of freedom
## Residual deviance: 5224462 on 760 degrees of freedom
## AIC: 10135
##
## Number of Fisher Scoring iterations: 13
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.Interact.X.bayesglm bayesglm
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.044 0.464
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.6462436 93.42035 0.6061056 133.6435 10134.83
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.5114766 2.6462 0.01574892
## label step_major step_minor bgn end elapsed
## 10 fit.models_1_bayesglm 10 0 166.159 169.315 3.156
## 11 fit.models_1_glmnet 11 0 169.315 NA NA
## [1] "fitting model: All.Interact.X.glmnet"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 1.26 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha
## Length Class Mode
## a0 100 -none- numeric
## beta 9900 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 99 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 1.954532e+02
## prdline.my.fctriPad 1
## -7.616609e+01
## prdline.my.fctriPad 2
## -1.250901e+01
## prdline.my.fctriPad 3+
## 1.394177e+01
## prdline.my.fctriPadAir
## 1.814492e+02
## prdline.my.fctriPadmini 2+
## 8.491691e+01
## condition.fctrFor parts or not working
## -4.984628e+01
## condition.fctrManufacturer refurbished
## -1.167462e+01
## condition.fctrNew
## 7.127744e+01
## condition.fctrNew other (see details)
## 4.201658e+01
## condition.fctrSeller refurbished
## -1.310733e+01
## color.fctrGold
## 2.919238e+00
## color.fctrSpace Gray
## 1.519904e+01
## color.fctrWhite
## 2.070700e+01
## D.TfIdf.sum.stem.stop.Ratio
## 4.045360e+01
## carrier.fctrAT&T
## -8.831998e-02
## carrier.fctrOther
## 7.960467e+01
## carrier.fctrSprint
## -3.977957e+01
## carrier.fctrVerizon
## 2.674020e+00
## storage.fctr16
## -5.043216e+01
## storage.fctr32
## -3.408977e+01
## storage.fctrUnknown
## -1.166875e+01
## cellular.fctr1
## 7.286834e+00
## cellular.fctrUnknown
## -3.029363e+01
## D.npnct08.log
## 5.693286e+00
## D.npnct05.log
## -5.488667e+01
## D.npnct16.log
## 1.731314e+01
## D.nstopwrds.log
## 7.322939e-01
## D.npnct11.log
## -1.108332e+01
## D.npnct13.log
## -3.829996e+00
## D.ratio.sum.TfIdf.nwrds
## -1.955522e+01
## biddable
## -1.226224e+02
## idseq.my
## -6.953370e-04
## prdline.my.fctriPad 1:biddable
## 3.710049e+01
## prdline.my.fctriPadAir:biddable
## -6.217397e+01
## prdline.my.fctriPadmini 2+:biddable
## -3.485019e+01
## prdline.my.fctriPadAir:idseq.my
## -3.015150e-02
## prdline.my.fctriPadmini 2+:idseq.my
## -1.520718e-02
## prdline.my.fctrUnknown:.clusterid.fctr2
## 6.142949e+01
## prdline.my.fctriPad 2:.clusterid.fctr2
## 3.127940e+00
## prdline.my.fctriPad 3+:.clusterid.fctr2
## 4.527211e-01
## prdline.my.fctriPadAir:.clusterid.fctr2
## -3.417839e+01
## prdline.my.fctriPadmini:.clusterid.fctr2
## 8.160272e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 6.551020e+01
## prdline.my.fctrUnknown:.clusterid.fctr3
## 6.754474e+00
## prdline.my.fctriPad 1:.clusterid.fctr3
## 3.662517e-01
## prdline.my.fctriPadAir:.clusterid.fctr3
## -7.022460e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -2.752281e+00
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 3.769357e+00
## prdline.my.fctriPad 2:.clusterid.fctr5
## 5.794217e+00
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 2.210306e+02
## prdline.my.fctriPad 1
## -7.598990e+01
## prdline.my.fctriPad 2
## -5.026286e+00
## prdline.my.fctriPad 3+
## 7.077533e+01
## prdline.my.fctriPadAir
## 2.606382e+02
## prdline.my.fctriPadmini
## 2.209896e+01
## prdline.my.fctriPadmini 2+
## 1.902123e+02
## condition.fctrFor parts or not working
## -6.007174e+01
## condition.fctrManufacturer refurbished
## -3.064514e+01
## condition.fctrNew
## 6.208215e+01
## condition.fctrNew other (see details)
## 4.963354e+01
## condition.fctrSeller refurbished
## -1.809247e+01
## color.fctrBlack
## 5.164669e+00
## color.fctrGold
## 8.782480e+00
## color.fctrSpace Gray
## 1.901066e+01
## color.fctrWhite
## 2.617586e+01
## D.TfIdf.sum.stem.stop.Ratio
## 6.100966e+01
## carrier.fctrAT&T
## -8.471296e+00
## carrier.fctrOther
## 1.190920e+02
## carrier.fctrSprint
## -6.193663e+01
## carrier.fctrT-Mobile
## 2.311256e+00
## carrier.fctrUnknown
## 4.092093e+00
## storage.fctr16
## -1.275931e+02
## storage.fctr32
## -1.161631e+02
## storage.fctr64
## -8.131442e+01
## storage.fctrUnknown
## -1.024172e+02
## D.npnct14.log
## -4.451536e+00
## D.terms.n.stem.stop.Ratio
## -7.519069e+00
## cellular.fctr1
## 1.332934e+01
## cellular.fctrUnknown
## -3.184620e+01
## D.ndgts.log
## -2.770238e+00
## .rnorm
## 1.283093e+00
## D.npnct08.log
## 1.026637e+01
## D.npnct05.log
## -1.015870e+02
## D.npnct15.log
## 5.922279e+00
## D.npnct01.log
## 5.093478e+00
## D.npnct16.log
## -1.197066e+01
## D.npnct12.log
## 3.335939e+00
## D.npnct06.log
## 5.399098e+01
## D.npnct03.log
## -3.409705e+01
## D.nstopwrds.log
## -2.664652e+01
## D.npnct11.log
## -3.097948e+01
## D.npnct13.log
## -1.938986e+01
## D.terms.n.post.stop
## -1.467494e+00
## D.nwrds.log
## 7.078326e+01
## D.terms.n.post.stop.log
## -1.236883e+01
## D.nwrds.unq.log
## -1.001130e-01
## D.terms.n.post.stem.log
## -1.122503e-16
## D.nchrs.log
## -2.043824e-01
## D.nuppr.log
## 1.689876e+01
## D.npnct24.log
## -2.511752e+02
## D.TfIdf.sum.post.stop
## 5.744841e-01
## D.ratio.sum.TfIdf.nwrds
## -1.671646e+00
## biddable
## -1.144949e+02
## idseq.my
## 3.166274e-02
## prdline.my.fctriPad 1:biddable
## 5.948388e+01
## prdline.my.fctriPad 2:biddable
## 5.446480e+00
## prdline.my.fctriPad 3+:biddable
## -2.180093e+01
## prdline.my.fctriPadAir:biddable
## -1.007927e+02
## prdline.my.fctriPadmini:biddable
## 4.426360e+00
## prdline.my.fctriPadmini 2+:biddable
## -8.359752e+01
## prdline.my.fctriPad 1:idseq.my
## -1.746218e-02
## prdline.my.fctriPad 2:idseq.my
## -1.201566e-02
## prdline.my.fctriPad 3+:idseq.my
## -4.140824e-02
## prdline.my.fctriPadAir:idseq.my
## -8.111589e-02
## prdline.my.fctriPadmini:idseq.my
## -2.560798e-02
## prdline.my.fctriPadmini 2+:idseq.my
## -9.144506e-02
## prdline.my.fctrUnknown:.clusterid.fctr2
## 9.093308e+01
## prdline.my.fctriPad 1:.clusterid.fctr2
## 1.820564e+01
## prdline.my.fctriPad 2:.clusterid.fctr2
## 3.092577e+01
## prdline.my.fctriPad 3+:.clusterid.fctr2
## 1.901364e+01
## prdline.my.fctriPadAir:.clusterid.fctr2
## -3.568664e+01
## prdline.my.fctriPadmini:.clusterid.fctr2
## 3.395920e+01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 7.482995e+01
## prdline.my.fctrUnknown:.clusterid.fctr3
## 4.824849e+01
## prdline.my.fctriPad 1:.clusterid.fctr3
## 2.965738e+01
## prdline.my.fctriPad 2:.clusterid.fctr3
## 4.339142e+00
## prdline.my.fctriPad 3+:.clusterid.fctr3
## -8.705339e-01
## prdline.my.fctriPadAir:.clusterid.fctr3
## -1.978734e+01
## prdline.my.fctriPadmini:.clusterid.fctr3
## 1.656144e+01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -8.169249e-01
## prdline.my.fctriPad 1:.clusterid.fctr4
## 1.767561e+01
## prdline.my.fctriPad 2:.clusterid.fctr4
## 7.024048e+00
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 2.433871e+01
## prdline.my.fctriPadAir:.clusterid.fctr4
## -1.463486e+00
## prdline.my.fctriPadmini:.clusterid.fctr4
## -2.197551e+00
## prdline.my.fctriPad 2:.clusterid.fctr5
## 4.916467e+01
## prdline.my.fctriPadmini:.clusterid.fctr5
## 1.783632e+01
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.Interact.X.glmnet glmnet
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 9 1.542 0.061
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.6154744 88.78737 0.609411 133.0815 0.5451064
## min.RMSESD.fit max.RsquaredSD.fit
## 1 4.086101 0.04518189
## label step_major step_minor bgn end elapsed
## 11 fit.models_1_glmnet 11 0 169.315 172.634 3.319
## 12 fit.models_1_rpart 12 0 172.634 NA NA
## [1] "fitting model: All.Interact.X.no.rnorm.rpart"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0827 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 860
##
## CP nsplit rel error
## 1 0.22941102 0 1.000000
## 2 0.08271687 1 0.770589
##
## Variable importance
## biddable idseq.my
## 87 4
## prdline.my.fctriPadmini 2+:idseq.my D.npnct15.log
## 4 2
## prdline.my.fctriPad 1:idseq.my prdline.my.fctriPadAir:idseq.my
## 2 1
##
## Node number 1: 860 observations, complexity param=0.229411
## mean=127.4371, MSE=17172.71
## left son=2 (640 obs) right son=3 (220 obs)
## Primary splits:
## biddable < 0.5 to the right, improve=0.22941100, (0 missing)
## prdline.my.fctriPadAir:idseq.my < 9.5 to the left, improve=0.14781390, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.14781390, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.13039270, (0 missing)
## condition.fctrFor parts or not working < 0.5 to the right, improve=0.05958729, (0 missing)
## Surrogate splits:
## idseq.my < 1783.5 to the left, agree=0.757, adj=0.050, (0 split)
## prdline.my.fctriPadmini 2+:idseq.my < 1420.5 to the left, agree=0.756, adj=0.045, (0 split)
## D.npnct15.log < 0.3465736 to the left, agree=0.750, adj=0.023, (0 split)
## prdline.my.fctriPad 1:idseq.my < 1666.5 to the left, agree=0.750, adj=0.023, (0 split)
## prdline.my.fctriPadAir:idseq.my < 1796.5 to the left, agree=0.748, adj=0.014, (0 split)
##
## Node number 2: 640 observations
## mean=90.63711, MSE=11139.65
##
## Node number 3: 220 observations
## mean=234.4917, MSE=19323.14
##
## n= 860
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 860 14768530 127.43710
## 2) biddable>=0.5 640 7129375 90.63711 *
## 3) biddable< 0.5 220 4251091 234.49170 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.Interact.X.no.rnorm.rpart rpart
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.518 0.066
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.229411 112.2342 0.315937 176.1188 0.2702555
## min.RMSESD.fit max.RsquaredSD.fit
## 1 3.565753 0.04046247
## label step_major step_minor bgn end elapsed
## 12 fit.models_1_rpart 12 0 172.634 176.319 3.685
## 13 fit.models_1_rf 13 0 176.320 NA NA
## [1] "fitting model: All.Interact.X.no.rnorm.rf"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 50 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 860 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 860 -none- numeric
## importance 98 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 860 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 98 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.Interact.X.no.rnorm.rf rf
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 27.199 9.436
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.8966 92.2999 0.6070976 133.3578 0.5140052
## min.RMSESD.fit max.RsquaredSD.fit
## 1 5.011737 0.04418812# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# model_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", model_id), select=feats)
# , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
model_id <- "csm"; indep_vars_vctr <- c(NULL
,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
,"prdline.my.fctr*biddable"
#,"prdline.my.fctr*startprice.log"
#,"prdline.my.fctr*startprice.diff"
#,"prdline.my.fctr*idseq.my"
,"prdline.my.fctr*condition.fctr"
,"prdline.my.fctr*D.terms.n.post.stop"
#,"prdline.my.fctr*D.terms.n.post.stem"
,"prdline.my.fctr*cellular.fctr"
# ,"<feat1>:<feat2>"
)
for (method in glb_models_method_vctr) {
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
csm_mdl_id <- paste0(model_id, ".", method)
csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".",
method)]]); print(head(csm_featsimp_df))
}## [1] "fitting model: csm.lm"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: not plotting observations with leverage one:
## 361, 442, 462, 532, 665
## Warning: not plotting observations with leverage one:
## 361, 442, 462, 532, 665
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -268.89 -39.41 -2.19 36.28 380.12
##
## Coefficients: (9 not defined because of singularities)
## Estimate
## (Intercept) 188.73461
## `prdline.my.fctriPad 1` -101.66016
## `prdline.my.fctriPad 2` -27.64071
## `prdline.my.fctriPad 3+` 35.94397
## prdline.my.fctriPadAir 219.14914
## prdline.my.fctriPadmini -14.96015
## `prdline.my.fctriPadmini 2+` 115.54506
## biddable -146.34794
## `condition.fctrFor parts or not working` -25.20856
## `condition.fctrManufacturer refurbished` -7.05259
## condition.fctrNew 86.71396
## `condition.fctrNew other (see details)` -59.81733
## `condition.fctrSeller refurbished` -27.78478
## D.terms.n.post.stop 0.15573
## cellular.fctr1 114.65592
## cellular.fctrUnknown -5.27976
## `prdline.my.fctrUnknown:.clusterid.fctr2` 79.27409
## `prdline.my.fctriPad 1:.clusterid.fctr2` 6.93818
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.49440
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -8.40538
## `prdline.my.fctriPadAir:.clusterid.fctr2` -86.22332
## `prdline.my.fctriPadmini:.clusterid.fctr2` 4.46324
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 70.41386
## `prdline.my.fctrUnknown:.clusterid.fctr3` 11.38819
## `prdline.my.fctriPad 1:.clusterid.fctr3` -10.10189
## `prdline.my.fctriPad 2:.clusterid.fctr3` 22.86397
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -27.34226
## `prdline.my.fctriPadAir:.clusterid.fctr3` -50.24103
## `prdline.my.fctriPadmini:.clusterid.fctr3` 4.78963
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -43.51418
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -2.92972
## `prdline.my.fctriPad 2:.clusterid.fctr4` -4.24860
## `prdline.my.fctriPad 3+:.clusterid.fctr4` -2.66651
## `prdline.my.fctriPadAir:.clusterid.fctr4` -42.22725
## `prdline.my.fctriPadmini:.clusterid.fctr4` 11.45600
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 23.88474
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 24.83800
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 100.62991
## `prdline.my.fctriPad 2:biddable` 41.61615
## `prdline.my.fctriPad 3+:biddable` 22.48382
## `prdline.my.fctriPadAir:biddable` -64.48690
## `prdline.my.fctriPadmini:biddable` 41.86750
## `prdline.my.fctriPadmini 2+:biddable` -43.90979
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` -0.23326
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 4.28718
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -58.57744
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -120.21908
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -32.70464
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -114.71610
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -41.72637
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 15.82806
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -63.74915
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` -50.93788
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 49.86368
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -105.97934
## `prdline.my.fctriPad 1:condition.fctrNew` 6.21160
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` -61.39254
## `prdline.my.fctriPadAir:condition.fctrNew` -26.22928
## `prdline.my.fctriPadmini:condition.fctrNew` -12.76722
## `prdline.my.fctriPadmini 2+:condition.fctrNew` -36.12573
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 11.53271
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 50.45413
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 137.67349
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 106.27528
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 72.22742
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 192.62993
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` -0.59755
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 28.49657
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 8.32857
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -162.60750
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 77.87474
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` NA
## `prdline.my.fctriPad 1:D.terms.n.post.stop` -0.03323
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -1.20694
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -0.83649
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 1.62564
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -0.74443
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` -2.67122
## `prdline.my.fctriPad 1:cellular.fctr1` -106.24337
## `prdline.my.fctriPad 2:cellular.fctr1` -103.74156
## `prdline.my.fctriPad 3+:cellular.fctr1` -102.55549
## `prdline.my.fctriPadAir:cellular.fctr1` -102.31131
## `prdline.my.fctriPadmini:cellular.fctr1` -104.11021
## `prdline.my.fctriPadmini 2+:cellular.fctr1` -48.96506
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 15.13944
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -20.15811
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 6.07720
## `prdline.my.fctriPadAir:cellular.fctrUnknown` -105.17594
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 27.10106
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 9.29464
## Std. Error
## (Intercept) 29.44501
## `prdline.my.fctriPad 1` 35.46740
## `prdline.my.fctriPad 2` 35.49646
## `prdline.my.fctriPad 3+` 35.93021
## prdline.my.fctriPadAir 34.59393
## prdline.my.fctriPadmini 35.80789
## `prdline.my.fctriPadmini 2+` 38.03604
## biddable 22.12596
## `condition.fctrFor parts or not working` 26.00720
## `condition.fctrManufacturer refurbished` 111.08182
## condition.fctrNew 32.65375
## `condition.fctrNew other (see details)` 59.82281
## `condition.fctrSeller refurbished` 52.83496
## D.terms.n.post.stop 3.47370
## cellular.fctr1 71.67199
## cellular.fctrUnknown 25.98202
## `prdline.my.fctrUnknown:.clusterid.fctr2` 32.38701
## `prdline.my.fctriPad 1:.clusterid.fctr2` 37.00606
## `prdline.my.fctriPad 2:.clusterid.fctr2` 24.38964
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 30.44501
## `prdline.my.fctriPadAir:.clusterid.fctr2` 27.56860
## `prdline.my.fctriPadmini:.clusterid.fctr2` 36.13566
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 58.36123
## `prdline.my.fctrUnknown:.clusterid.fctr3` 43.27255
## `prdline.my.fctriPad 1:.clusterid.fctr3` 35.44052
## `prdline.my.fctriPad 2:.clusterid.fctr3` 45.76700
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 28.44538
## `prdline.my.fctriPadAir:.clusterid.fctr3` 37.66888
## `prdline.my.fctriPadmini:.clusterid.fctr3` 33.77006
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 49.84948
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 41.70860
## `prdline.my.fctriPad 2:.clusterid.fctr4` 32.12650
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 29.63474
## `prdline.my.fctriPadAir:.clusterid.fctr4` 37.29309
## `prdline.my.fctriPadmini:.clusterid.fctr4` 41.36373
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 36.51597
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 52.11354
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 29.65356
## `prdline.my.fctriPad 2:biddable` 29.04799
## `prdline.my.fctriPad 3+:biddable` 29.11098
## `prdline.my.fctriPadAir:biddable` 28.34147
## `prdline.my.fctriPadmini:biddable` 29.71108
## `prdline.my.fctriPadmini 2+:biddable` 31.72122
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 39.65939
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 34.66119
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 34.28835
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 39.86152
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 33.96842
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 69.79429
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 142.23185
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 122.39215
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 127.68376
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 120.74784
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 128.04182
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 142.39449
## `prdline.my.fctriPad 1:condition.fctrNew` 95.30993
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` 95.48312
## `prdline.my.fctriPadAir:condition.fctrNew` 37.91319
## `prdline.my.fctriPadmini:condition.fctrNew` 43.03081
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 41.09639
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 108.68734
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 80.69586
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 73.39503
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 65.15966
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 74.68091
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 88.32844
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 61.07084
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 61.96086
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 63.76821
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 75.69888
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 70.32456
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` NA
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 5.10976
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 4.26684
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 4.46555
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 4.59392
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 5.21042
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 7.32044
## `prdline.my.fctriPad 1:cellular.fctr1` 73.69180
## `prdline.my.fctriPad 2:cellular.fctr1` 73.98578
## `prdline.my.fctriPad 3+:cellular.fctr1` 73.53515
## `prdline.my.fctriPadAir:cellular.fctr1` 73.48735
## `prdline.my.fctriPadmini:cellular.fctr1` 74.62228
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 75.31320
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 49.06573
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 58.71215
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 46.36919
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 46.30320
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 58.96521
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 49.73867
## t value
## (Intercept) 6.410
## `prdline.my.fctriPad 1` -2.866
## `prdline.my.fctriPad 2` -0.779
## `prdline.my.fctriPad 3+` 1.000
## prdline.my.fctriPadAir 6.335
## prdline.my.fctriPadmini -0.418
## `prdline.my.fctriPadmini 2+` 3.038
## biddable -6.614
## `condition.fctrFor parts or not working` -0.969
## `condition.fctrManufacturer refurbished` -0.063
## condition.fctrNew 2.656
## `condition.fctrNew other (see details)` -1.000
## `condition.fctrSeller refurbished` -0.526
## D.terms.n.post.stop 0.045
## cellular.fctr1 1.600
## cellular.fctrUnknown -0.203
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.448
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.187
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.020
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -0.276
## `prdline.my.fctriPadAir:.clusterid.fctr2` -3.128
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.124
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.207
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.263
## `prdline.my.fctriPad 1:.clusterid.fctr3` -0.285
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.500
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.961
## `prdline.my.fctriPadAir:.clusterid.fctr3` -1.334
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.142
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.873
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -0.070
## `prdline.my.fctriPad 2:.clusterid.fctr4` -0.132
## `prdline.my.fctriPad 3+:.clusterid.fctr4` -0.090
## `prdline.my.fctriPadAir:.clusterid.fctr4` -1.132
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.277
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.654
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.477
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 3.394
## `prdline.my.fctriPad 2:biddable` 1.433
## `prdline.my.fctriPad 3+:biddable` 0.772
## `prdline.my.fctriPadAir:biddable` -2.275
## `prdline.my.fctriPadmini:biddable` 1.409
## `prdline.my.fctriPadmini 2+:biddable` -1.384
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` -0.006
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.124
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -1.708
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -3.016
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -0.963
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -1.644
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -0.293
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.129
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -0.499
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` -0.422
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.389
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -0.744
## `prdline.my.fctriPad 1:condition.fctrNew` 0.065
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` -0.643
## `prdline.my.fctriPadAir:condition.fctrNew` -0.692
## `prdline.my.fctriPadmini:condition.fctrNew` -0.297
## `prdline.my.fctriPadmini 2+:condition.fctrNew` -0.879
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 0.106
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 0.625
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 1.876
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 1.631
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.967
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 2.181
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` -0.010
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.460
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.131
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -2.148
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 1.107
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` NA
## `prdline.my.fctriPad 1:D.terms.n.post.stop` -0.007
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -0.283
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -0.187
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.354
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -0.143
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` -0.365
## `prdline.my.fctriPad 1:cellular.fctr1` -1.442
## `prdline.my.fctriPad 2:cellular.fctr1` -1.402
## `prdline.my.fctriPad 3+:cellular.fctr1` -1.395
## `prdline.my.fctriPadAir:cellular.fctr1` -1.392
## `prdline.my.fctriPadmini:cellular.fctr1` -1.395
## `prdline.my.fctriPadmini 2+:cellular.fctr1` -0.650
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.309
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -0.343
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 0.131
## `prdline.my.fctriPadAir:cellular.fctrUnknown` -2.271
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.460
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.187
## Pr(>|t|)
## (Intercept) 2.54e-10
## `prdline.my.fctriPad 1` 0.004266
## `prdline.my.fctriPad 2` 0.436402
## `prdline.my.fctriPad 3+` 0.317439
## prdline.my.fctriPadAir 4.03e-10
## prdline.my.fctriPadmini 0.676218
## `prdline.my.fctriPadmini 2+` 0.002464
## biddable 6.98e-11
## `condition.fctrFor parts or not working` 0.332704
## `condition.fctrManufacturer refurbished` 0.949393
## condition.fctrNew 0.008081
## `condition.fctrNew other (see details)` 0.317669
## `condition.fctrSeller refurbished` 0.599124
## D.terms.n.post.stop 0.964253
## cellular.fctr1 0.110068
## cellular.fctrUnknown 0.839026
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.014599
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.851328
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.983832
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.782557
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.001829
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.901733
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.227988
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.792487
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.775692
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.617518
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.336743
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.182678
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.887251
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.382983
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.944019
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.894824
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.928327
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.257857
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.781887
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.513249
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.633773
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 0.000725
## `prdline.my.fctriPad 2:biddable` 0.152358
## `prdline.my.fctriPad 3+:biddable` 0.440145
## `prdline.my.fctriPadAir:biddable` 0.023158
## `prdline.my.fctriPadmini:biddable` 0.159193
## `prdline.my.fctriPadmini 2+:biddable` 0.166685
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.995309
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.901594
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 0.087969
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 0.002646
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.335952
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 0.100660
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 0.769319
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.897136
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.617729
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.673250
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.697064
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 0.456942
## `prdline.my.fctriPad 1:condition.fctrNew` 0.948053
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` 0.520436
## `prdline.my.fctriPadAir:condition.fctrNew` 0.489256
## `prdline.my.fctriPadmini:condition.fctrNew` 0.766776
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.379649
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 0.915523
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 0.532000
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.061062
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.103299
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.333774
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 0.029497
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.992196
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.645709
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.896120
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 0.032018
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.268483
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` NA
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.994813
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 0.777356
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 0.851459
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.723535
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.886427
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.715287
## `prdline.my.fctriPad 1:cellular.fctr1` 0.149786
## `prdline.my.fctriPad 2:cellular.fctr1` 0.161263
## `prdline.my.fctriPad 3+:cellular.fctr1` 0.163524
## `prdline.my.fctriPadAir:cellular.fctr1` 0.164254
## `prdline.my.fctriPadmini:cellular.fctr1` 0.163369
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 0.515787
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.757744
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 0.731438
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 0.895761
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 0.023394
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.645925
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.851812
##
## (Intercept) ***
## `prdline.my.fctriPad 1` **
## `prdline.my.fctriPad 2`
## `prdline.my.fctriPad 3+`
## prdline.my.fctriPadAir ***
## prdline.my.fctriPadmini
## `prdline.my.fctriPadmini 2+` **
## biddable ***
## `condition.fctrFor parts or not working`
## `condition.fctrManufacturer refurbished`
## condition.fctrNew **
## `condition.fctrNew other (see details)`
## `condition.fctrSeller refurbished`
## D.terms.n.post.stop
## cellular.fctr1
## cellular.fctrUnknown
## `prdline.my.fctrUnknown:.clusterid.fctr2` *
## `prdline.my.fctriPad 1:.clusterid.fctr2`
## `prdline.my.fctriPad 2:.clusterid.fctr2`
## `prdline.my.fctriPad 3+:.clusterid.fctr2`
## `prdline.my.fctriPadAir:.clusterid.fctr2` **
## `prdline.my.fctriPadmini:.clusterid.fctr2`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2`
## `prdline.my.fctrUnknown:.clusterid.fctr3`
## `prdline.my.fctriPad 1:.clusterid.fctr3`
## `prdline.my.fctriPad 2:.clusterid.fctr3`
## `prdline.my.fctriPad 3+:.clusterid.fctr3`
## `prdline.my.fctriPadAir:.clusterid.fctr3`
## `prdline.my.fctriPadmini:.clusterid.fctr3`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3`
## `prdline.my.fctrUnknown:.clusterid.fctr4`
## `prdline.my.fctriPad 1:.clusterid.fctr4`
## `prdline.my.fctriPad 2:.clusterid.fctr4`
## `prdline.my.fctriPad 3+:.clusterid.fctr4`
## `prdline.my.fctriPadAir:.clusterid.fctr4`
## `prdline.my.fctriPadmini:.clusterid.fctr4`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4`
## `prdline.my.fctrUnknown:.clusterid.fctr5`
## `prdline.my.fctriPad 1:.clusterid.fctr5`
## `prdline.my.fctriPad 2:.clusterid.fctr5`
## `prdline.my.fctriPad 3+:.clusterid.fctr5`
## `prdline.my.fctriPadAir:.clusterid.fctr5`
## `prdline.my.fctriPadmini:.clusterid.fctr5`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5`
## `prdline.my.fctriPad 1:biddable` ***
## `prdline.my.fctriPad 2:biddable`
## `prdline.my.fctriPad 3+:biddable`
## `prdline.my.fctriPadAir:biddable` *
## `prdline.my.fctriPadmini:biddable`
## `prdline.my.fctriPadmini 2+:biddable`
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` .
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` **
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working`
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 1:condition.fctrNew`
## `prdline.my.fctriPad 2:condition.fctrNew`
## `prdline.my.fctriPad 3+:condition.fctrNew`
## `prdline.my.fctriPadAir:condition.fctrNew`
## `prdline.my.fctriPadmini:condition.fctrNew`
## `prdline.my.fctriPadmini 2+:condition.fctrNew`
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` .
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` *
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished`
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` *
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 1:D.terms.n.post.stop`
## `prdline.my.fctriPad 2:D.terms.n.post.stop`
## `prdline.my.fctriPad 3+:D.terms.n.post.stop`
## `prdline.my.fctriPadAir:D.terms.n.post.stop`
## `prdline.my.fctriPadmini:D.terms.n.post.stop`
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop`
## `prdline.my.fctriPad 1:cellular.fctr1`
## `prdline.my.fctriPad 2:cellular.fctr1`
## `prdline.my.fctriPad 3+:cellular.fctr1`
## `prdline.my.fctriPadAir:cellular.fctr1`
## `prdline.my.fctriPadmini:cellular.fctr1`
## `prdline.my.fctriPadmini 2+:cellular.fctr1`
## `prdline.my.fctriPad 1:cellular.fctrUnknown`
## `prdline.my.fctriPad 2:cellular.fctrUnknown`
## `prdline.my.fctriPad 3+:cellular.fctrUnknown`
## `prdline.my.fctriPadAir:cellular.fctrUnknown` *
## `prdline.my.fctriPadmini:cellular.fctrUnknown`
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 87.33 on 771 degrees of freedom
## Multiple R-squared: 0.6018, Adjusted R-squared: 0.5564
## F-statistic: 13.24 on 88 and 771 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## model_id model_method
## 1 csm.lm lm
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.133 0.046
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.6018389 96.42778 0.5974058 135.1112 0.5563938
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.4781052 8.600697 0.07746978
## importance
## biddable 100.00000
## prdline.my.fctriPadAir 95.77194
## `prdline.my.fctriPad 1:biddable` 51.26237
## `prdline.my.fctriPadAir:.clusterid.fctr2` 47.23832
## `prdline.my.fctriPadmini 2+` 45.87923
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 45.54844
## [1] "fitting model: csm.glm"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: not plotting observations with leverage one:
## 361, 442, 462, 532, 665
## Warning: not plotting observations with leverage one:
## 361, 442, 462, 532, 665
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -268.89 -39.41 -2.19 36.28 380.12
##
## Coefficients: (9 not defined because of singularities)
## Estimate
## (Intercept) 188.73461
## `prdline.my.fctriPad 1` -101.66016
## `prdline.my.fctriPad 2` -27.64071
## `prdline.my.fctriPad 3+` 35.94397
## prdline.my.fctriPadAir 219.14914
## prdline.my.fctriPadmini -14.96015
## `prdline.my.fctriPadmini 2+` 115.54506
## biddable -146.34794
## `condition.fctrFor parts or not working` -25.20856
## `condition.fctrManufacturer refurbished` -7.05259
## condition.fctrNew 86.71396
## `condition.fctrNew other (see details)` -59.81733
## `condition.fctrSeller refurbished` -27.78478
## D.terms.n.post.stop 0.15573
## cellular.fctr1 114.65592
## cellular.fctrUnknown -5.27976
## `prdline.my.fctrUnknown:.clusterid.fctr2` 79.27409
## `prdline.my.fctriPad 1:.clusterid.fctr2` 6.93818
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.49440
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -8.40538
## `prdline.my.fctriPadAir:.clusterid.fctr2` -86.22332
## `prdline.my.fctriPadmini:.clusterid.fctr2` 4.46324
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 70.41386
## `prdline.my.fctrUnknown:.clusterid.fctr3` 11.38819
## `prdline.my.fctriPad 1:.clusterid.fctr3` -10.10189
## `prdline.my.fctriPad 2:.clusterid.fctr3` 22.86397
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -27.34226
## `prdline.my.fctriPadAir:.clusterid.fctr3` -50.24103
## `prdline.my.fctriPadmini:.clusterid.fctr3` 4.78963
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -43.51418
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -2.92972
## `prdline.my.fctriPad 2:.clusterid.fctr4` -4.24860
## `prdline.my.fctriPad 3+:.clusterid.fctr4` -2.66651
## `prdline.my.fctriPadAir:.clusterid.fctr4` -42.22725
## `prdline.my.fctriPadmini:.clusterid.fctr4` 11.45600
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 23.88474
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 24.83800
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 100.62991
## `prdline.my.fctriPad 2:biddable` 41.61615
## `prdline.my.fctriPad 3+:biddable` 22.48382
## `prdline.my.fctriPadAir:biddable` -64.48690
## `prdline.my.fctriPadmini:biddable` 41.86750
## `prdline.my.fctriPadmini 2+:biddable` -43.90979
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` -0.23326
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 4.28718
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -58.57744
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -120.21908
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -32.70464
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -114.71610
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -41.72637
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 15.82806
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -63.74915
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` -50.93788
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 49.86368
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -105.97934
## `prdline.my.fctriPad 1:condition.fctrNew` 6.21160
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` -61.39254
## `prdline.my.fctriPadAir:condition.fctrNew` -26.22928
## `prdline.my.fctriPadmini:condition.fctrNew` -12.76722
## `prdline.my.fctriPadmini 2+:condition.fctrNew` -36.12573
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 11.53271
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 50.45413
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 137.67349
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 106.27528
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 72.22742
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 192.62993
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` -0.59755
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 28.49657
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 8.32857
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -162.60750
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 77.87474
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` NA
## `prdline.my.fctriPad 1:D.terms.n.post.stop` -0.03323
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -1.20694
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -0.83649
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 1.62564
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -0.74443
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` -2.67122
## `prdline.my.fctriPad 1:cellular.fctr1` -106.24337
## `prdline.my.fctriPad 2:cellular.fctr1` -103.74156
## `prdline.my.fctriPad 3+:cellular.fctr1` -102.55549
## `prdline.my.fctriPadAir:cellular.fctr1` -102.31131
## `prdline.my.fctriPadmini:cellular.fctr1` -104.11021
## `prdline.my.fctriPadmini 2+:cellular.fctr1` -48.96506
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 15.13944
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -20.15811
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 6.07720
## `prdline.my.fctriPadAir:cellular.fctrUnknown` -105.17594
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 27.10106
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 9.29464
## Std. Error
## (Intercept) 29.44501
## `prdline.my.fctriPad 1` 35.46740
## `prdline.my.fctriPad 2` 35.49646
## `prdline.my.fctriPad 3+` 35.93021
## prdline.my.fctriPadAir 34.59393
## prdline.my.fctriPadmini 35.80789
## `prdline.my.fctriPadmini 2+` 38.03604
## biddable 22.12596
## `condition.fctrFor parts or not working` 26.00720
## `condition.fctrManufacturer refurbished` 111.08182
## condition.fctrNew 32.65375
## `condition.fctrNew other (see details)` 59.82281
## `condition.fctrSeller refurbished` 52.83496
## D.terms.n.post.stop 3.47370
## cellular.fctr1 71.67199
## cellular.fctrUnknown 25.98202
## `prdline.my.fctrUnknown:.clusterid.fctr2` 32.38701
## `prdline.my.fctriPad 1:.clusterid.fctr2` 37.00606
## `prdline.my.fctriPad 2:.clusterid.fctr2` 24.38964
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 30.44501
## `prdline.my.fctriPadAir:.clusterid.fctr2` 27.56860
## `prdline.my.fctriPadmini:.clusterid.fctr2` 36.13566
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 58.36123
## `prdline.my.fctrUnknown:.clusterid.fctr3` 43.27255
## `prdline.my.fctriPad 1:.clusterid.fctr3` 35.44052
## `prdline.my.fctriPad 2:.clusterid.fctr3` 45.76700
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 28.44538
## `prdline.my.fctriPadAir:.clusterid.fctr3` 37.66888
## `prdline.my.fctriPadmini:.clusterid.fctr3` 33.77006
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 49.84948
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 41.70860
## `prdline.my.fctriPad 2:.clusterid.fctr4` 32.12650
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 29.63474
## `prdline.my.fctriPadAir:.clusterid.fctr4` 37.29309
## `prdline.my.fctriPadmini:.clusterid.fctr4` 41.36373
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 36.51597
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 52.11354
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 29.65356
## `prdline.my.fctriPad 2:biddable` 29.04799
## `prdline.my.fctriPad 3+:biddable` 29.11098
## `prdline.my.fctriPadAir:biddable` 28.34147
## `prdline.my.fctriPadmini:biddable` 29.71108
## `prdline.my.fctriPadmini 2+:biddable` 31.72122
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 39.65939
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 34.66119
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 34.28835
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 39.86152
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 33.96842
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 69.79429
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 142.23185
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 122.39215
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 127.68376
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 120.74784
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 128.04182
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 142.39449
## `prdline.my.fctriPad 1:condition.fctrNew` 95.30993
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` 95.48312
## `prdline.my.fctriPadAir:condition.fctrNew` 37.91319
## `prdline.my.fctriPadmini:condition.fctrNew` 43.03081
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 41.09639
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 108.68734
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 80.69586
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 73.39503
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 65.15966
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 74.68091
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 88.32844
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 61.07084
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 61.96086
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 63.76821
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 75.69888
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 70.32456
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` NA
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 5.10976
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 4.26684
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 4.46555
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 4.59392
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 5.21042
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 7.32044
## `prdline.my.fctriPad 1:cellular.fctr1` 73.69180
## `prdline.my.fctriPad 2:cellular.fctr1` 73.98578
## `prdline.my.fctriPad 3+:cellular.fctr1` 73.53515
## `prdline.my.fctriPadAir:cellular.fctr1` 73.48735
## `prdline.my.fctriPadmini:cellular.fctr1` 74.62228
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 75.31320
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 49.06573
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 58.71215
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 46.36919
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 46.30320
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 58.96521
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 49.73867
## t value
## (Intercept) 6.410
## `prdline.my.fctriPad 1` -2.866
## `prdline.my.fctriPad 2` -0.779
## `prdline.my.fctriPad 3+` 1.000
## prdline.my.fctriPadAir 6.335
## prdline.my.fctriPadmini -0.418
## `prdline.my.fctriPadmini 2+` 3.038
## biddable -6.614
## `condition.fctrFor parts or not working` -0.969
## `condition.fctrManufacturer refurbished` -0.063
## condition.fctrNew 2.656
## `condition.fctrNew other (see details)` -1.000
## `condition.fctrSeller refurbished` -0.526
## D.terms.n.post.stop 0.045
## cellular.fctr1 1.600
## cellular.fctrUnknown -0.203
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.448
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.187
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.020
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -0.276
## `prdline.my.fctriPadAir:.clusterid.fctr2` -3.128
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.124
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.207
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.263
## `prdline.my.fctriPad 1:.clusterid.fctr3` -0.285
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.500
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.961
## `prdline.my.fctriPadAir:.clusterid.fctr3` -1.334
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.142
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.873
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -0.070
## `prdline.my.fctriPad 2:.clusterid.fctr4` -0.132
## `prdline.my.fctriPad 3+:.clusterid.fctr4` -0.090
## `prdline.my.fctriPadAir:.clusterid.fctr4` -1.132
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.277
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.654
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.477
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 3.394
## `prdline.my.fctriPad 2:biddable` 1.433
## `prdline.my.fctriPad 3+:biddable` 0.772
## `prdline.my.fctriPadAir:biddable` -2.275
## `prdline.my.fctriPadmini:biddable` 1.409
## `prdline.my.fctriPadmini 2+:biddable` -1.384
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` -0.006
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.124
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -1.708
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -3.016
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -0.963
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -1.644
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -0.293
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.129
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -0.499
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` -0.422
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.389
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -0.744
## `prdline.my.fctriPad 1:condition.fctrNew` 0.065
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` -0.643
## `prdline.my.fctriPadAir:condition.fctrNew` -0.692
## `prdline.my.fctriPadmini:condition.fctrNew` -0.297
## `prdline.my.fctriPadmini 2+:condition.fctrNew` -0.879
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 0.106
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 0.625
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 1.876
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 1.631
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.967
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 2.181
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` -0.010
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.460
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.131
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -2.148
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 1.107
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` NA
## `prdline.my.fctriPad 1:D.terms.n.post.stop` -0.007
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -0.283
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -0.187
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.354
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -0.143
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` -0.365
## `prdline.my.fctriPad 1:cellular.fctr1` -1.442
## `prdline.my.fctriPad 2:cellular.fctr1` -1.402
## `prdline.my.fctriPad 3+:cellular.fctr1` -1.395
## `prdline.my.fctriPadAir:cellular.fctr1` -1.392
## `prdline.my.fctriPadmini:cellular.fctr1` -1.395
## `prdline.my.fctriPadmini 2+:cellular.fctr1` -0.650
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.309
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -0.343
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 0.131
## `prdline.my.fctriPadAir:cellular.fctrUnknown` -2.271
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.460
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.187
## Pr(>|t|)
## (Intercept) 2.54e-10
## `prdline.my.fctriPad 1` 0.004266
## `prdline.my.fctriPad 2` 0.436402
## `prdline.my.fctriPad 3+` 0.317439
## prdline.my.fctriPadAir 4.03e-10
## prdline.my.fctriPadmini 0.676218
## `prdline.my.fctriPadmini 2+` 0.002464
## biddable 6.98e-11
## `condition.fctrFor parts or not working` 0.332704
## `condition.fctrManufacturer refurbished` 0.949393
## condition.fctrNew 0.008081
## `condition.fctrNew other (see details)` 0.317669
## `condition.fctrSeller refurbished` 0.599124
## D.terms.n.post.stop 0.964253
## cellular.fctr1 0.110068
## cellular.fctrUnknown 0.839026
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.014599
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.851328
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.983832
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.782557
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.001829
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.901733
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.227988
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.792487
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.775692
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.617518
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.336743
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.182678
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.887251
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.382983
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.944019
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.894824
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.928327
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.257857
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.781887
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.513249
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.633773
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 0.000725
## `prdline.my.fctriPad 2:biddable` 0.152358
## `prdline.my.fctriPad 3+:biddable` 0.440145
## `prdline.my.fctriPadAir:biddable` 0.023158
## `prdline.my.fctriPadmini:biddable` 0.159193
## `prdline.my.fctriPadmini 2+:biddable` 0.166685
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.995309
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.901594
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 0.087969
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 0.002646
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.335952
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 0.100660
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 0.769319
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.897136
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.617729
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.673250
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.697064
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 0.456942
## `prdline.my.fctriPad 1:condition.fctrNew` 0.948053
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` 0.520436
## `prdline.my.fctriPadAir:condition.fctrNew` 0.489256
## `prdline.my.fctriPadmini:condition.fctrNew` 0.766776
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.379649
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 0.915523
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 0.532000
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.061062
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.103299
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.333774
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 0.029497
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.992196
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.645709
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.896120
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 0.032018
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.268483
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` NA
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.994813
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 0.777356
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 0.851459
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.723535
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.886427
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.715287
## `prdline.my.fctriPad 1:cellular.fctr1` 0.149786
## `prdline.my.fctriPad 2:cellular.fctr1` 0.161263
## `prdline.my.fctriPad 3+:cellular.fctr1` 0.163524
## `prdline.my.fctriPadAir:cellular.fctr1` 0.164254
## `prdline.my.fctriPadmini:cellular.fctr1` 0.163369
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 0.515787
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.757744
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 0.731438
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 0.895761
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 0.023394
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.645925
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.851812
##
## (Intercept) ***
## `prdline.my.fctriPad 1` **
## `prdline.my.fctriPad 2`
## `prdline.my.fctriPad 3+`
## prdline.my.fctriPadAir ***
## prdline.my.fctriPadmini
## `prdline.my.fctriPadmini 2+` **
## biddable ***
## `condition.fctrFor parts or not working`
## `condition.fctrManufacturer refurbished`
## condition.fctrNew **
## `condition.fctrNew other (see details)`
## `condition.fctrSeller refurbished`
## D.terms.n.post.stop
## cellular.fctr1
## cellular.fctrUnknown
## `prdline.my.fctrUnknown:.clusterid.fctr2` *
## `prdline.my.fctriPad 1:.clusterid.fctr2`
## `prdline.my.fctriPad 2:.clusterid.fctr2`
## `prdline.my.fctriPad 3+:.clusterid.fctr2`
## `prdline.my.fctriPadAir:.clusterid.fctr2` **
## `prdline.my.fctriPadmini:.clusterid.fctr2`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2`
## `prdline.my.fctrUnknown:.clusterid.fctr3`
## `prdline.my.fctriPad 1:.clusterid.fctr3`
## `prdline.my.fctriPad 2:.clusterid.fctr3`
## `prdline.my.fctriPad 3+:.clusterid.fctr3`
## `prdline.my.fctriPadAir:.clusterid.fctr3`
## `prdline.my.fctriPadmini:.clusterid.fctr3`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3`
## `prdline.my.fctrUnknown:.clusterid.fctr4`
## `prdline.my.fctriPad 1:.clusterid.fctr4`
## `prdline.my.fctriPad 2:.clusterid.fctr4`
## `prdline.my.fctriPad 3+:.clusterid.fctr4`
## `prdline.my.fctriPadAir:.clusterid.fctr4`
## `prdline.my.fctriPadmini:.clusterid.fctr4`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4`
## `prdline.my.fctrUnknown:.clusterid.fctr5`
## `prdline.my.fctriPad 1:.clusterid.fctr5`
## `prdline.my.fctriPad 2:.clusterid.fctr5`
## `prdline.my.fctriPad 3+:.clusterid.fctr5`
## `prdline.my.fctriPadAir:.clusterid.fctr5`
## `prdline.my.fctriPadmini:.clusterid.fctr5`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5`
## `prdline.my.fctriPad 1:biddable` ***
## `prdline.my.fctriPad 2:biddable`
## `prdline.my.fctriPad 3+:biddable`
## `prdline.my.fctriPadAir:biddable` *
## `prdline.my.fctriPadmini:biddable`
## `prdline.my.fctriPadmini 2+:biddable`
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` .
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` **
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working`
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 1:condition.fctrNew`
## `prdline.my.fctriPad 2:condition.fctrNew`
## `prdline.my.fctriPad 3+:condition.fctrNew`
## `prdline.my.fctriPadAir:condition.fctrNew`
## `prdline.my.fctriPadmini:condition.fctrNew`
## `prdline.my.fctriPadmini 2+:condition.fctrNew`
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` .
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` *
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished`
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` *
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 1:D.terms.n.post.stop`
## `prdline.my.fctriPad 2:D.terms.n.post.stop`
## `prdline.my.fctriPad 3+:D.terms.n.post.stop`
## `prdline.my.fctriPadAir:D.terms.n.post.stop`
## `prdline.my.fctriPadmini:D.terms.n.post.stop`
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop`
## `prdline.my.fctriPad 1:cellular.fctr1`
## `prdline.my.fctriPad 2:cellular.fctr1`
## `prdline.my.fctriPad 3+:cellular.fctr1`
## `prdline.my.fctriPadAir:cellular.fctr1`
## `prdline.my.fctriPadmini:cellular.fctr1`
## `prdline.my.fctriPadmini 2+:cellular.fctr1`
## `prdline.my.fctriPad 1:cellular.fctrUnknown`
## `prdline.my.fctriPad 2:cellular.fctrUnknown`
## `prdline.my.fctriPad 3+:cellular.fctrUnknown`
## `prdline.my.fctriPadAir:cellular.fctrUnknown` *
## `prdline.my.fctriPadmini:cellular.fctrUnknown`
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 7626.788)
##
## Null deviance: 14768530 on 859 degrees of freedom
## Residual deviance: 5880254 on 771 degrees of freedom
## AIC: 10215
##
## Number of Fisher Scoring iterations: 2
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## model_id model_method
## 1 csm.glm glm
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.218 0.064
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.6018389 96.42778 0.5974058 135.1112 10214.53
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.4781052 8.600697 0.07746978
## importance
## biddable 100.00000
## prdline.my.fctriPadAir 95.77194
## `prdline.my.fctriPad 1:biddable` 51.26237
## `prdline.my.fctriPadAir:.clusterid.fctr2` 47.23832
## `prdline.my.fctriPadmini 2+` 45.87923
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 45.54844
## [1] "fitting model: csm.bayesglm"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -269.09 -39.59 -2.42 36.02 379.92
##
## Coefficients:
## Estimate
## (Intercept) 191.3221
## `prdline.my.fctriPad 1` -103.9421
## `prdline.my.fctriPad 2` -30.1235
## `prdline.my.fctriPad 3+` 33.1966
## prdline.my.fctriPadAir 215.9854
## prdline.my.fctriPadmini -17.3522
## `prdline.my.fctriPadmini 2+` 112.4398
## biddable -145.9463
## `condition.fctrFor parts or not working` -26.3235
## `condition.fctrManufacturer refurbished` -3.0619
## condition.fctrNew 84.9105
## `condition.fctrNew other (see details)` -47.8695
## `condition.fctrSeller refurbished` -27.8801
## D.terms.n.post.stop 0.2376
## cellular.fctr1 100.5596
## cellular.fctrUnknown -7.4494
## `prdline.my.fctrUnknown:.clusterid.fctr2` 76.3918
## `prdline.my.fctriPad 1:.clusterid.fctr2` 6.9385
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.4717
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -8.2498
## `prdline.my.fctriPadAir:.clusterid.fctr2` -85.4211
## `prdline.my.fctriPadmini:.clusterid.fctr2` 4.4246
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 70.0556
## `prdline.my.fctrUnknown:.clusterid.fctr3` 10.1324
## `prdline.my.fctriPad 1:.clusterid.fctr3` -9.9961
## `prdline.my.fctriPad 2:.clusterid.fctr3` 22.6944
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -27.1800
## `prdline.my.fctriPadAir:.clusterid.fctr3` -49.3201
## `prdline.my.fctriPadmini:.clusterid.fctr3` 4.6730
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -43.4462
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.0000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -2.8557
## `prdline.my.fctriPad 2:.clusterid.fctr4` -4.2823
## `prdline.my.fctriPad 3+:.clusterid.fctr4` -2.5440
## `prdline.my.fctriPadAir:.clusterid.fctr4` -41.3036
## `prdline.my.fctriPadmini:.clusterid.fctr4` 11.4204
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.0000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.0000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.0000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 23.7269
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.0000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.0000
## `prdline.my.fctriPadmini:.clusterid.fctr5` 24.4650
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.0000
## `prdline.my.fctriPad 1:biddable` 99.8792
## `prdline.my.fctriPad 2:biddable` 41.1195
## `prdline.my.fctriPad 3+:biddable` 22.1192
## `prdline.my.fctriPadAir:biddable` -64.5713
## `prdline.my.fctriPadmini:biddable` 41.2718
## `prdline.my.fctriPadmini 2+:biddable` -43.9784
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.7589
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 5.3456
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -57.3139
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -118.6636
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -31.5336
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -111.5239
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -44.2059
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 11.6384
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -66.5787
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` -53.7475
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 44.9218
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -106.2432
## `prdline.my.fctriPad 1:condition.fctrNew` 7.4504
## `prdline.my.fctriPad 2:condition.fctrNew` 0.0000
## `prdline.my.fctriPad 3+:condition.fctrNew` -57.4491
## `prdline.my.fctriPadAir:condition.fctrNew` -24.0866
## `prdline.my.fctriPadmini:condition.fctrNew` -10.9655
## `prdline.my.fctriPadmini 2+:condition.fctrNew` -33.9132
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -0.3049
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 38.1197
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 124.7681
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 94.2965
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 59.7049
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 177.9936
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` -0.5739
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 28.4382
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 8.4627
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -160.1935
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 77.0716
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 0.0000
## `prdline.my.fctriPad 1:D.terms.n.post.stop` -0.1222
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -1.2883
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -0.9254
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 1.4666
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -0.8185
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` -2.6999
## `prdline.my.fctriPad 1:cellular.fctr1` -92.1102
## `prdline.my.fctriPad 2:cellular.fctr1` -89.5583
## `prdline.my.fctriPad 3+:cellular.fctr1` -88.2277
## `prdline.my.fctriPadAir:cellular.fctr1` -88.0946
## `prdline.my.fctriPadmini:cellular.fctr1` -89.8733
## `prdline.my.fctriPadmini 2+:cellular.fctr1` -34.7506
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 17.2255
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -17.8294
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 8.2937
## `prdline.my.fctriPadAir:cellular.fctrUnknown` -102.2777
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 28.8298
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 11.3445
## Std. Error
## (Intercept) 28.9019
## `prdline.my.fctriPad 1` 34.9787
## `prdline.my.fctriPad 2` 35.0065
## `prdline.my.fctriPad 3+` 35.4244
## prdline.my.fctriPadAir 34.1096
## prdline.my.fctriPadmini 35.3138
## `prdline.my.fctriPadmini 2+` 37.5731
## biddable 21.9795
## `condition.fctrFor parts or not working` 25.7989
## `condition.fctrManufacturer refurbished` 93.3005
## condition.fctrNew 32.2945
## `condition.fctrNew other (see details)` 56.1872
## `condition.fctrSeller refurbished` 51.1955
## D.terms.n.post.stop 3.4812
## cellular.fctr1 63.5954
## cellular.fctrUnknown 25.5227
## `prdline.my.fctrUnknown:.clusterid.fctr2` 32.2446
## `prdline.my.fctriPad 1:.clusterid.fctr2` 37.0054
## `prdline.my.fctriPad 2:.clusterid.fctr2` 24.4701
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 30.5411
## `prdline.my.fctriPadAir:.clusterid.fctr2` 27.6382
## `prdline.my.fctriPadmini:.clusterid.fctr2` 36.1247
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 58.0776
## `prdline.my.fctrUnknown:.clusterid.fctr3` 43.3046
## `prdline.my.fctriPad 1:.clusterid.fctr3` 35.4738
## `prdline.my.fctriPad 2:.clusterid.fctr3` 45.7707
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 28.5430
## `prdline.my.fctriPadAir:.clusterid.fctr3` 37.7196
## `prdline.my.fctriPadmini:.clusterid.fctr3` 33.7616
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 49.6768
## `prdline.my.fctrUnknown:.clusterid.fctr4` 659.9990
## `prdline.my.fctriPad 1:.clusterid.fctr4` 41.7137
## `prdline.my.fctriPad 2:.clusterid.fctr4` 32.2039
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 29.7305
## `prdline.my.fctriPadAir:.clusterid.fctr4` 37.3520
## `prdline.my.fctriPadmini:.clusterid.fctr4` 41.3178
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 659.9990
## `prdline.my.fctrUnknown:.clusterid.fctr5` 659.9990
## `prdline.my.fctriPad 1:.clusterid.fctr5` 659.9990
## `prdline.my.fctriPad 2:.clusterid.fctr5` 36.5892
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 659.9990
## `prdline.my.fctriPadAir:.clusterid.fctr5` 659.9990
## `prdline.my.fctriPadmini:.clusterid.fctr5` 52.0151
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 659.9990
## `prdline.my.fctriPad 1:biddable` 29.5468
## `prdline.my.fctriPad 2:biddable` 28.9403
## `prdline.my.fctriPad 3+:biddable` 28.9926
## `prdline.my.fctriPadAir:biddable` 28.2459
## `prdline.my.fctriPadmini:biddable` 29.6024
## `prdline.my.fctriPadmini 2+:biddable` 31.6195
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 39.4981
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 34.5201
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 34.1497
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 39.7329
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 33.8133
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 69.3225
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 125.8208
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 105.5473
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 111.0992
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 103.8017
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 111.4576
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 126.0722
## `prdline.my.fctriPad 1:condition.fctrNew` 93.7852
## `prdline.my.fctriPad 2:condition.fctrNew` 659.9990
## `prdline.my.fctriPad 3+:condition.fctrNew` 93.9595
## `prdline.my.fctriPadAir:condition.fctrNew` 37.5929
## `prdline.my.fctriPadmini:condition.fctrNew` 42.7344
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 40.7731
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 104.7226
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 77.4692
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 70.1299
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 61.7288
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 71.4200
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 85.0974
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 59.5183
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 60.4206
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 62.2292
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 74.0926
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 68.7569
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 659.9990
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 5.1170
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 4.2775
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 4.4778
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 4.6047
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 5.2134
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 7.3067
## `prdline.my.fctriPad 1:cellular.fctr1` 65.7682
## `prdline.my.fctriPad 2:cellular.fctr1` 66.1211
## `prdline.my.fctriPad 3+:cellular.fctr1` 65.6064
## `prdline.my.fctriPadAir:cellular.fctr1` 65.5794
## `prdline.my.fctriPadmini:cellular.fctr1` 66.7964
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 67.5905
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 48.7828
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 58.3308
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 46.0801
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 46.0247
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 58.5865
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 49.4457
## t value
## (Intercept) 6.620
## `prdline.my.fctriPad 1` -2.972
## `prdline.my.fctriPad 2` -0.861
## `prdline.my.fctriPad 3+` 0.937
## prdline.my.fctriPadAir 6.332
## prdline.my.fctriPadmini -0.491
## `prdline.my.fctriPadmini 2+` 2.993
## biddable -6.640
## `condition.fctrFor parts or not working` -1.020
## `condition.fctrManufacturer refurbished` -0.033
## condition.fctrNew 2.629
## `condition.fctrNew other (see details)` -0.852
## `condition.fctrSeller refurbished` -0.545
## D.terms.n.post.stop 0.068
## cellular.fctr1 1.581
## cellular.fctrUnknown -0.292
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.369
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.187
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.019
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -0.270
## `prdline.my.fctriPadAir:.clusterid.fctr2` -3.091
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.122
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.206
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.234
## `prdline.my.fctriPad 1:.clusterid.fctr3` -0.282
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.496
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.952
## `prdline.my.fctriPadAir:.clusterid.fctr3` -1.308
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.138
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.875
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -0.068
## `prdline.my.fctriPad 2:.clusterid.fctr4` -0.133
## `prdline.my.fctriPad 3+:.clusterid.fctr4` -0.086
## `prdline.my.fctriPadAir:.clusterid.fctr4` -1.106
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.276
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.648
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.470
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000
## `prdline.my.fctriPad 1:biddable` 3.380
## `prdline.my.fctriPad 2:biddable` 1.421
## `prdline.my.fctriPad 3+:biddable` 0.763
## `prdline.my.fctriPadAir:biddable` -2.286
## `prdline.my.fctriPadmini:biddable` 1.394
## `prdline.my.fctriPadmini 2+:biddable` -1.391
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.019
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.155
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -1.678
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -2.987
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -0.933
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -1.609
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -0.351
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.110
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -0.599
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` -0.518
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.403
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -0.843
## `prdline.my.fctriPad 1:condition.fctrNew` 0.079
## `prdline.my.fctriPad 2:condition.fctrNew` 0.000
## `prdline.my.fctriPad 3+:condition.fctrNew` -0.611
## `prdline.my.fctriPadAir:condition.fctrNew` -0.641
## `prdline.my.fctriPadmini:condition.fctrNew` -0.257
## `prdline.my.fctriPadmini 2+:condition.fctrNew` -0.832
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -0.003
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 0.492
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 1.779
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 1.528
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.836
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 2.092
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` -0.010
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.471
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.136
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -2.162
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 1.121
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 0.000
## `prdline.my.fctriPad 1:D.terms.n.post.stop` -0.024
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -0.301
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -0.207
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.318
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -0.157
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` -0.370
## `prdline.my.fctriPad 1:cellular.fctr1` -1.401
## `prdline.my.fctriPad 2:cellular.fctr1` -1.354
## `prdline.my.fctriPad 3+:cellular.fctr1` -1.345
## `prdline.my.fctriPadAir:cellular.fctr1` -1.343
## `prdline.my.fctriPadmini:cellular.fctr1` -1.345
## `prdline.my.fctriPadmini 2+:cellular.fctr1` -0.514
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.353
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -0.306
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 0.180
## `prdline.my.fctriPadAir:cellular.fctrUnknown` -2.222
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.492
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.229
## Pr(>|t|)
## (Intercept) 6.79e-11
## `prdline.my.fctriPad 1` 0.003056
## `prdline.my.fctriPad 2` 0.389778
## `prdline.my.fctriPad 3+` 0.348999
## prdline.my.fctriPadAir 4.13e-10
## prdline.my.fctriPadmini 0.623304
## `prdline.my.fctriPadmini 2+` 0.002856
## biddable 5.96e-11
## `condition.fctrFor parts or not working` 0.307892
## `condition.fctrManufacturer refurbished` 0.973829
## condition.fctrNew 0.008730
## `condition.fctrNew other (see details)` 0.394501
## `condition.fctrSeller refurbished` 0.586201
## D.terms.n.post.stop 0.945601
## cellular.fctr1 0.114238
## cellular.fctrUnknown 0.770462
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.018078
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.851319
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.984625
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.787141
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.002070
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.902550
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.228099
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.815064
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.778182
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.620159
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.341272
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.191422
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.889952
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.382079
## `prdline.my.fctrUnknown:.clusterid.fctr4` 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.945437
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.894249
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.931833
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.269164
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.782313
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 1.000000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 1.000000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.516878
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 1.000000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 1.000000
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.638243
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 1.000000
## `prdline.my.fctriPad 1:biddable` 0.000761
## `prdline.my.fctriPad 2:biddable` 0.155773
## `prdline.my.fctriPad 3+:biddable` 0.445745
## `prdline.my.fctriPadAir:biddable` 0.022525
## `prdline.my.fctriPadmini:biddable` 0.163663
## `prdline.my.fctriPadmini 2+:biddable` 0.164673
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.984676
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.876976
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 0.093696
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 0.002912
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.351333
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 0.108081
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 0.725430
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.912227
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.549169
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.604755
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.687032
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 0.399651
## `prdline.my.fctriPad 1:condition.fctrNew` 0.936702
## `prdline.my.fctriPad 2:condition.fctrNew` 1.000000
## `prdline.my.fctriPad 3+:condition.fctrNew` 0.541101
## `prdline.my.fctriPadAir:condition.fctrNew` 0.521895
## `prdline.my.fctriPadmini:condition.fctrNew` 0.797560
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.405807
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 0.997678
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 0.622817
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.075622
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.127028
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.403435
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 0.036801
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.992309
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.638010
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.891863
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 0.030923
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.262671
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 1.000000
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.980954
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 0.763359
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 0.836324
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.750196
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.875288
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.711855
## `prdline.my.fctriPad 1:cellular.fctr1` 0.161762
## `prdline.my.fctriPad 2:cellular.fctr1` 0.175992
## `prdline.my.fctriPad 3+:cellular.fctr1` 0.179089
## `prdline.my.fctriPadAir:cellular.fctr1` 0.179566
## `prdline.my.fctriPadmini:cellular.fctr1` 0.178870
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 0.607306
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.724107
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 0.759947
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 0.857213
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 0.026560
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.622797
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.818594
##
## (Intercept) ***
## `prdline.my.fctriPad 1` **
## `prdline.my.fctriPad 2`
## `prdline.my.fctriPad 3+`
## prdline.my.fctriPadAir ***
## prdline.my.fctriPadmini
## `prdline.my.fctriPadmini 2+` **
## biddable ***
## `condition.fctrFor parts or not working`
## `condition.fctrManufacturer refurbished`
## condition.fctrNew **
## `condition.fctrNew other (see details)`
## `condition.fctrSeller refurbished`
## D.terms.n.post.stop
## cellular.fctr1
## cellular.fctrUnknown
## `prdline.my.fctrUnknown:.clusterid.fctr2` *
## `prdline.my.fctriPad 1:.clusterid.fctr2`
## `prdline.my.fctriPad 2:.clusterid.fctr2`
## `prdline.my.fctriPad 3+:.clusterid.fctr2`
## `prdline.my.fctriPadAir:.clusterid.fctr2` **
## `prdline.my.fctriPadmini:.clusterid.fctr2`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2`
## `prdline.my.fctrUnknown:.clusterid.fctr3`
## `prdline.my.fctriPad 1:.clusterid.fctr3`
## `prdline.my.fctriPad 2:.clusterid.fctr3`
## `prdline.my.fctriPad 3+:.clusterid.fctr3`
## `prdline.my.fctriPadAir:.clusterid.fctr3`
## `prdline.my.fctriPadmini:.clusterid.fctr3`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3`
## `prdline.my.fctrUnknown:.clusterid.fctr4`
## `prdline.my.fctriPad 1:.clusterid.fctr4`
## `prdline.my.fctriPad 2:.clusterid.fctr4`
## `prdline.my.fctriPad 3+:.clusterid.fctr4`
## `prdline.my.fctriPadAir:.clusterid.fctr4`
## `prdline.my.fctriPadmini:.clusterid.fctr4`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4`
## `prdline.my.fctrUnknown:.clusterid.fctr5`
## `prdline.my.fctriPad 1:.clusterid.fctr5`
## `prdline.my.fctriPad 2:.clusterid.fctr5`
## `prdline.my.fctriPad 3+:.clusterid.fctr5`
## `prdline.my.fctriPadAir:.clusterid.fctr5`
## `prdline.my.fctriPadmini:.clusterid.fctr5`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5`
## `prdline.my.fctriPad 1:biddable` ***
## `prdline.my.fctriPad 2:biddable`
## `prdline.my.fctriPad 3+:biddable`
## `prdline.my.fctriPadAir:biddable` *
## `prdline.my.fctriPadmini:biddable`
## `prdline.my.fctriPadmini 2+:biddable`
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` .
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` **
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working`
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 1:condition.fctrNew`
## `prdline.my.fctriPad 2:condition.fctrNew`
## `prdline.my.fctriPad 3+:condition.fctrNew`
## `prdline.my.fctriPadAir:condition.fctrNew`
## `prdline.my.fctriPadmini:condition.fctrNew`
## `prdline.my.fctriPadmini 2+:condition.fctrNew`
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` .
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` *
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished`
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` *
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 1:D.terms.n.post.stop`
## `prdline.my.fctriPad 2:D.terms.n.post.stop`
## `prdline.my.fctriPad 3+:D.terms.n.post.stop`
## `prdline.my.fctriPadAir:D.terms.n.post.stop`
## `prdline.my.fctriPadmini:D.terms.n.post.stop`
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop`
## `prdline.my.fctriPad 1:cellular.fctr1`
## `prdline.my.fctriPad 2:cellular.fctr1`
## `prdline.my.fctriPad 3+:cellular.fctr1`
## `prdline.my.fctriPadAir:cellular.fctr1`
## `prdline.my.fctriPadmini:cellular.fctr1`
## `prdline.my.fctriPadmini 2+:cellular.fctr1`
## `prdline.my.fctriPad 1:cellular.fctrUnknown`
## `prdline.my.fctriPad 2:cellular.fctrUnknown`
## `prdline.my.fctriPad 3+:cellular.fctrUnknown`
## `prdline.my.fctriPadAir:cellular.fctrUnknown` *
## `prdline.my.fctriPadmini:cellular.fctrUnknown`
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 7717.689)
##
## Null deviance: 14768530 on 859 degrees of freedom
## Residual deviance: 5880879 on 762 degrees of freedom
## AIC: 10233
##
## Number of Fisher Scoring iterations: 9
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 csm.bayesglm bayesglm
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.781 0.328
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.6017966 95.5102 0.5989458 134.8527 10232.62
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.4855296 8.673136 0.08078789
## importance
## biddable 100.000000
## prdline.my.fctr 37.057533
## condition.fctr 18.485338
## D.terms.n.post.stop 3.739121
## .clusterid.fctr 1.592186
## cellular.fctr 0.000000
## [1] "fitting model: csm.glmnet"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 1.26 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha
## Length Class Mode
## a0 94 -none- numeric
## beta 9118 dgCMatrix S4
## df 94 -none- numeric
## dim 2 -none- numeric
## lambda 94 -none- numeric
## dev.ratio 94 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 97 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 189.23122560
## prdline.my.fctriPad 1
## -71.32344487
## prdline.my.fctriPad 2
## -10.38722148
## prdline.my.fctriPad 3+
## 24.04838998
## prdline.my.fctriPadAir
## 186.80555245
## prdline.my.fctriPadmini 2+
## 84.04137942
## biddable
## -120.23610262
## condition.fctrFor parts or not working
## -38.61900183
## condition.fctrNew
## 67.39913615
## condition.fctrNew other (see details)
## 1.11704431
## condition.fctrSeller refurbished
## -8.66222891
## D.terms.n.post.stop
## -0.27841239
## cellular.fctr1
## 8.26522252
## cellular.fctrUnknown
## -6.33256015
## prdline.my.fctrUnknown:.clusterid.fctr2
## 57.27408896
## prdline.my.fctriPadAir:.clusterid.fctr2
## -58.92493078
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 48.89180760
## prdline.my.fctriPad 3+:.clusterid.fctr3
## -12.83076670
## prdline.my.fctriPadAir:.clusterid.fctr3
## -13.34851602
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -30.26291995
## prdline.my.fctriPad 2:.clusterid.fctr4
## -3.99700403
## prdline.my.fctriPadAir:.clusterid.fctr4
## -5.75204376
## prdline.my.fctriPad 1:biddable
## 41.31971310
## prdline.my.fctriPadAir:biddable
## -60.80134999
## prdline.my.fctriPadmini 2+:biddable
## -37.27271604
## prdline.my.fctriPad 2:condition.fctrFor parts or not working
## 0.43998657
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working
## -31.77613685
## prdline.my.fctriPadAir:condition.fctrFor parts or not working
## -97.26947421
## prdline.my.fctriPadmini:condition.fctrFor parts or not working
## -7.64421947
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working
## -73.99600121
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished
## -9.30528476
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished
## -34.73381299
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished
## -2.04131817
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished
## 11.24131476
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished
## -77.81275163
## prdline.my.fctriPad 3+:condition.fctrNew other (see details)
## 68.99753414
## prdline.my.fctriPadAir:condition.fctrNew other (see details)
## 36.26750167
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)
## 107.88303407
## prdline.my.fctriPad 1:condition.fctrSeller refurbished
## -13.71656887
## prdline.my.fctriPadAir:condition.fctrSeller refurbished
## -147.58003974
## prdline.my.fctriPadmini:condition.fctrSeller refurbished
## 32.58743538
## prdline.my.fctriPad 2:D.terms.n.post.stop
## -0.08965563
## prdline.my.fctriPad 3+:cellular.fctr1
## 3.08184377
## prdline.my.fctriPadmini 2+:cellular.fctr1
## 47.08724496
## prdline.my.fctriPad 1:cellular.fctrUnknown
## 1.49696973
## prdline.my.fctriPad 2:cellular.fctrUnknown
## -8.09894373
## prdline.my.fctriPadAir:cellular.fctrUnknown
## -86.32380455
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 190.37943406
## prdline.my.fctriPad 1
## -102.91199342
## prdline.my.fctriPad 2
## -28.98532366
## prdline.my.fctriPad 3+
## 34.19327804
## prdline.my.fctriPadAir
## 217.22837681
## prdline.my.fctriPadmini
## -16.31404196
## prdline.my.fctriPadmini 2+
## 113.30684608
## biddable
## -145.35320492
## condition.fctrFor parts or not working
## -25.55915445
## condition.fctrManufacturer refurbished
## 10.44777336
## condition.fctrNew
## 85.05607255
## condition.fctrNew other (see details)
## -49.30248026
## condition.fctrSeller refurbished
## -26.85302850
## D.terms.n.post.stop
## 0.09529079
## cellular.fctr1
## 92.40084008
## cellular.fctrUnknown
## -7.08680774
## prdline.my.fctrUnknown:.clusterid.fctr2
## 78.24367117
## prdline.my.fctriPad 1:.clusterid.fctr2
## 7.00656370
## prdline.my.fctriPad 2:.clusterid.fctr2
## 0.23822476
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -8.24768943
## prdline.my.fctriPadAir:.clusterid.fctr2
## -85.71805957
## prdline.my.fctriPadmini:.clusterid.fctr2
## 3.75086270
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 69.84612587
## prdline.my.fctrUnknown:.clusterid.fctr3
## 10.48669112
## prdline.my.fctriPad 1:.clusterid.fctr3
## -9.81514992
## prdline.my.fctriPad 2:.clusterid.fctr3
## 22.42943890
## prdline.my.fctriPad 3+:.clusterid.fctr3
## -27.19063803
## prdline.my.fctriPadAir:.clusterid.fctr3
## -49.63879461
## prdline.my.fctriPadmini:.clusterid.fctr3
## 4.11850043
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -43.77665187
## prdline.my.fctriPad 1:.clusterid.fctr4
## -2.67135845
## prdline.my.fctriPad 2:.clusterid.fctr4
## -4.40664656
## prdline.my.fctriPad 3+:.clusterid.fctr4
## -2.49919084
## prdline.my.fctriPadAir:.clusterid.fctr4
## -41.58457317
## prdline.my.fctriPadmini:.clusterid.fctr4
## 10.67380310
## prdline.my.fctriPad 2:.clusterid.fctr5
## 23.47907172
## prdline.my.fctriPadmini:.clusterid.fctr5
## 23.98310240
## prdline.my.fctriPad 1:biddable
## 99.21920924
## prdline.my.fctriPad 2:biddable
## 40.34880941
## prdline.my.fctriPad 3+:biddable
## 21.43318648
## prdline.my.fctriPadAir:biddable
## -65.28568144
## prdline.my.fctriPadmini:biddable
## 40.56267498
## prdline.my.fctriPadmini 2+:biddable
## -44.47408601
## prdline.my.fctriPad 2:condition.fctrFor parts or not working
## 4.45697073
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working
## -58.11711466
## prdline.my.fctriPadAir:condition.fctrFor parts or not working
## -119.76542984
## prdline.my.fctriPadmini:condition.fctrFor parts or not working
## -32.25958505
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working
## -113.96847580
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished
## -58.98522014
## prdline.my.fctriPad 2:condition.fctrManufacturer refurbished
## -1.59044722
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished
## -81.01938000
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished
## -67.89398923
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished
## 32.02188624
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished
## -122.96977640
## prdline.my.fctriPad 1:condition.fctrNew
## 7.18053260
## prdline.my.fctriPad 3+:condition.fctrNew
## -59.28176125
## prdline.my.fctriPadAir:condition.fctrNew
## -24.40689747
## prdline.my.fctriPadmini:condition.fctrNew
## -11.02464037
## prdline.my.fctriPadmini 2+:condition.fctrNew
## -34.00588259
## prdline.my.fctriPad 1:condition.fctrNew other (see details)
## 0.89316272
## prdline.my.fctriPad 2:condition.fctrNew other (see details)
## 39.87400815
## prdline.my.fctriPad 3+:condition.fctrNew other (see details)
## 126.82395782
## prdline.my.fctriPadAir:condition.fctrNew other (see details)
## 95.72315814
## prdline.my.fctriPadmini:condition.fctrNew other (see details)
## 61.66918517
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)
## 182.10229357
## prdline.my.fctriPad 1:condition.fctrSeller refurbished
## -1.55097725
## prdline.my.fctriPad 2:condition.fctrSeller refurbished
## 27.44613524
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished
## 7.31076658
## prdline.my.fctriPadAir:condition.fctrSeller refurbished
## -163.06502606
## prdline.my.fctriPadmini:condition.fctrSeller refurbished
## 76.58803751
## prdline.my.fctriPad 2:D.terms.n.post.stop
## -1.12408402
## prdline.my.fctriPad 3+:D.terms.n.post.stop
## -0.77548424
## prdline.my.fctriPadAir:D.terms.n.post.stop
## 1.63018046
## prdline.my.fctriPadmini:D.terms.n.post.stop
## -0.60221180
## prdline.my.fctriPadmini 2+:D.terms.n.post.stop
## -2.52425644
## prdline.my.fctriPad 1:cellular.fctr1
## -83.92886306
## prdline.my.fctriPad 2:cellular.fctr1
## -81.51908400
## prdline.my.fctriPad 3+:cellular.fctr1
## -80.11959456
## prdline.my.fctriPadAir:cellular.fctr1
## -79.96529578
## prdline.my.fctriPadmini:cellular.fctr1
## -81.87225636
## prdline.my.fctriPadmini 2+:cellular.fctr1
## -26.57282733
## prdline.my.fctriPad 1:cellular.fctrUnknown
## 16.83953755
## prdline.my.fctriPad 2:cellular.fctrUnknown
## -18.34861878
## prdline.my.fctriPad 3+:cellular.fctrUnknown
## 7.80719370
## prdline.my.fctriPadAir:cellular.fctrUnknown
## -103.16678441
## prdline.my.fctriPadmini:cellular.fctrUnknown
## 28.57554464
## prdline.my.fctriPadmini 2+:cellular.fctrUnknown
## 10.85331271
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 csm.glmnet glmnet
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 9 1.335 0.027
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.5891504 91.694 0.6021921 134.3057 0.5142438
## min.RMSESD.fit max.RsquaredSD.fit
## 1 8.875314 0.09451454
## importance
## prdline.my.fctriPadAir 100.00000
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details) 76.40506
## prdline.my.fctriPadmini 2+ 69.27759
## prdline.my.fctriPad 3+:condition.fctrNew other (see details) 64.76258
## condition.fctrNew 64.26390
## prdline.my.fctrUnknown:.clusterid.fctr2 61.25954
## [1] "fitting model: csm.rpart"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0803 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 860
##
## CP nsplit rel error
## 1 0.22941102 0 1.0000000
## 2 0.08271687 1 0.7705890
## 3 0.08034499 2 0.6878721
##
## Variable importance
## biddable
## 57
## prdline.my.fctriPadAir
## 20
## prdline.my.fctriPadAir:D.terms.n.post.stop
## 6
## prdline.my.fctriPadAir:condition.fctrNew
## 6
## prdline.my.fctriPadAir:cellular.fctr1
## 4
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished
## 3
## prdline.my.fctriPadAir:.clusterid.fctr2
## 2
## prdline.my.fctriPadmini 2+:condition.fctrNew
## 1
## prdline.my.fctriPadmini:cellular.fctr1
## 1
## prdline.my.fctriPadmini:condition.fctrNew other (see details)
## 1
## prdline.my.fctriPad 2:.clusterid.fctr5
## 1
##
## Node number 1: 860 observations, complexity param=0.229411
## mean=127.4371, MSE=17172.71
## left son=2 (640 obs) right son=3 (220 obs)
## Primary splits:
## biddable < 0.5 to the right, improve=0.22941100, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.14781390, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.13039270, (0 missing)
## prdline.my.fctriPadAir:condition.fctrNew < 0.5 to the left, improve=0.09284548, (0 missing)
## condition.fctrFor parts or not working < 0.5 to the right, improve=0.05958729, (0 missing)
## Surrogate splits:
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished < 0.5 to the left, agree=0.749, adj=0.018, (0 split)
## prdline.my.fctriPadmini 2+:condition.fctrNew < 0.5 to the left, agree=0.749, adj=0.018, (0 split)
## prdline.my.fctriPadmini:condition.fctrNew other (see details) < 0.5 to the left, agree=0.748, adj=0.014, (0 split)
## prdline.my.fctriPadmini:cellular.fctr1 < 0.5 to the left, agree=0.748, adj=0.014, (0 split)
## prdline.my.fctriPad 2:.clusterid.fctr5 < 0.5 to the left, agree=0.747, adj=0.009, (0 split)
##
## Node number 2: 640 observations
## mean=90.63711, MSE=11139.65
##
## Node number 3: 220 observations, complexity param=0.08271687
## mean=234.4917, MSE=19323.14
## left son=6 (183 obs) right son=7 (37 obs)
## Primary splits:
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.2873631, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.1844482, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.1762407, (0 missing)
## condition.fctrFor parts or not working < 0.5 to the right, improve=0.1396280, (0 missing)
## prdline.my.fctriPadAir:condition.fctrNew < 0.5 to the left, improve=0.1295271, (0 missing)
## Surrogate splits:
## prdline.my.fctriPadAir:condition.fctrNew < 0.5 to the left, agree=0.877, adj=0.270, (0 split)
## prdline.my.fctriPadAir:D.terms.n.post.stop < 1 to the left, agree=0.877, adj=0.270, (0 split)
## prdline.my.fctriPadAir:cellular.fctr1 < 0.5 to the left, agree=0.864, adj=0.189, (0 split)
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished < 0.5 to the left, agree=0.850, adj=0.108, (0 split)
## prdline.my.fctriPadAir:.clusterid.fctr2 < 0.5 to the left, agree=0.845, adj=0.081, (0 split)
##
## Node number 6: 183 observations
## mean=200.9851, MSE=13424.58
##
## Node number 7: 37 observations
## mean=400.2132, MSE=15480.72
##
## n= 860
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 860 14768530.0 127.43710
## 2) biddable>=0.5 640 7129375.0 90.63711 *
## 3) biddable< 0.5 220 4251091.0 234.49170
## 6) prdline.my.fctriPadAir< 0.5 183 2456698.0 200.98510 *
## 7) prdline.my.fctriPadAir>=0.5 37 572786.8 400.21320 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 csm.rpart rpart
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.522 0.063
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.3121279 111.8385 0.450545 157.8425 0.2750573
## min.RMSESD.fit max.RsquaredSD.fit
## 1 3.592112 0.04148092
## importance
## prdline.my.fctriPadAir 100.00000
## condition.fctrNew 72.34777
## biddable 52.71672
## prdline.my.fctriPadAir:condition.fctrNew 51.09936
## condition.fctrFor parts or not working 45.77800
## prdline.my.fctriPad 1 40.49864
## [1] "fitting model: csm.rf"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 49 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 860 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 860 -none- numeric
## importance 97 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 860 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 97 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 csm.rf rf
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 22.499 7.617
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.6913385 97.97158 0.6189452 131.3387 0.4641526
## min.RMSESD.fit max.RsquaredSD.fit
## 1 11.78792 0.1121849
## importance
## biddable 100.00000
## prdline.my.fctriPadAir 51.78248
## condition.fctrNew 25.63268
## prdline.my.fctriPadAir:biddable 15.64356
## D.terms.n.post.stop 15.33621
## condition.fctrFor parts or not working 14.04054# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glb_trnobs_df); print(summary(Ntv.1.lm))
#print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#csm_OOBobs_df <- glb_get_predictions(glb_OOBobs_df, mdl_id=csm_mdl_id, rsp_var_out=glb_rsp_var_out, prob_threshold_def=glb_models_df[glb_models_df$model_id == csm_mdl_id, "opt.prob.threshold.OOB"])
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
#print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id)); print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], glb_OOBobs_df[, glb_rsp_var])$table))
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## MFO.lm MFO.lm lm
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.lm Max.cor.Y.lm lm
## Interact.High.cor.Y.lm Interact.High.cor.Y.lm lm
## Low.cor.X.lm Low.cor.X.lm lm
## All.X.lm All.X.lm lm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.glmnet All.X.glmnet glmnet
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## All.Interact.X.lm All.Interact.X.lm lm
## All.Interact.X.glm All.Interact.X.glm glm
## All.Interact.X.bayesglm All.Interact.X.bayesglm bayesglm
## All.Interact.X.glmnet All.Interact.X.glmnet glmnet
## All.Interact.X.no.rnorm.rpart All.Interact.X.no.rnorm.rpart rpart
## All.Interact.X.no.rnorm.rf All.Interact.X.no.rnorm.rf rf
## csm.lm csm.lm lm
## csm.glm csm.glm glm
## csm.bayesglm csm.bayesglm bayesglm
## csm.glmnet csm.glmnet glmnet
## csm.rpart csm.rpart rpart
## csm.rf csm.rf rf
## feats
## MFO.lm .rnorm
## Max.cor.Y.cv.0.rpart biddable, prdline.my.fctr
## Max.cor.Y.cv.0.cp.0.rpart biddable, prdline.my.fctr
## Max.cor.Y.rpart biddable, prdline.my.fctr
## Max.cor.Y.lm biddable, prdline.my.fctr
## Interact.High.cor.Y.lm biddable, prdline.my.fctr, biddable:D.TfIdf.sum.post.stop, biddable:D.npnct06.log, biddable:D.npnct03.log, biddable:D.terms.n.post.stem, biddable:D.nuppr.log, biddable:D.nwrds.unq.log, biddable:D.npnct24.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.TfIdf.sum.post.stem
## Low.cor.X.lm prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct12.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.lm prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.glm prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.bayesglm prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.glmnet prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rpart prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rf prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.lm prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glm prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.bayesglm prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glmnet prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rpart prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rf prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## csm.lm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.glm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.bayesglm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.glmnet prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.rpart prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.rf prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything
## MFO.lm 0 0.513
## Max.cor.Y.cv.0.rpart 0 0.649
## Max.cor.Y.cv.0.cp.0.rpart 0 0.478
## Max.cor.Y.rpart 3 1.031
## Max.cor.Y.lm 1 0.960
## Interact.High.cor.Y.lm 1 0.970
## Low.cor.X.lm 1 1.123
## All.X.lm 1 1.177
## All.X.glm 1 1.070
## All.X.bayesglm 1 2.318
## All.X.glmnet 9 1.591
## All.X.no.rnorm.rpart 3 1.441
## All.X.no.rnorm.rf 3 24.469
## All.Interact.X.lm 1 1.156
## All.Interact.X.glm 1 1.198
## All.Interact.X.bayesglm 1 2.044
## All.Interact.X.glmnet 9 1.542
## All.Interact.X.no.rnorm.rpart 3 1.518
## All.Interact.X.no.rnorm.rf 3 27.199
## csm.lm 1 1.133
## csm.glm 1 1.218
## csm.bayesglm 1 1.781
## csm.glmnet 9 1.335
## csm.rpart 3 1.522
## csm.rf 3 22.499
## min.elapsedtime.final max.R.sq.fit
## MFO.lm 0.003 7.226357e-05
## Max.cor.Y.cv.0.rpart 0.015 0.000000e+00
## Max.cor.Y.cv.0.cp.0.rpart 0.009 4.923724e-01
## Max.cor.Y.rpart 0.012 3.121279e-01
## Max.cor.Y.lm 0.005 4.594170e-01
## Interact.High.cor.Y.lm 0.009 4.734947e-01
## Low.cor.X.lm 0.032 6.072324e-01
## All.X.lm 0.040 6.162904e-01
## All.X.glm 0.057 6.162904e-01
## All.X.bayesglm 0.385 6.156165e-01
## All.X.glmnet 0.047 5.836410e-01
## All.X.no.rnorm.rpart 0.059 3.121279e-01
## All.X.no.rnorm.rf 8.461 8.931938e-01
## All.Interact.X.lm 0.045 6.467622e-01
## All.Interact.X.glm 0.064 6.467622e-01
## All.Interact.X.bayesglm 0.464 6.462436e-01
## All.Interact.X.glmnet 0.061 6.154744e-01
## All.Interact.X.no.rnorm.rpart 0.066 2.294110e-01
## All.Interact.X.no.rnorm.rf 9.436 8.966000e-01
## csm.lm 0.046 6.018389e-01
## csm.glm 0.064 6.018389e-01
## csm.bayesglm 0.328 6.017966e-01
## csm.glmnet 0.027 5.891504e-01
## csm.rpart 0.063 3.121279e-01
## csm.rf 7.617 6.913385e-01
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## MFO.lm 131.03995 0.0001316983 212.9262
## Max.cor.Y.cv.0.rpart 131.04468 0.0000000000 212.9402
## Max.cor.Y.cv.0.cp.0.rpart 93.36670 0.5489639195 143.0090
## Max.cor.Y.rpart 111.83847 0.4505450166 157.8425
## Max.cor.Y.lm 97.12892 0.5186350614 147.7389
## Interact.High.cor.Y.lm 96.62961 0.5214588759 147.3050
## Low.cor.X.lm 92.54651 0.6201210933 131.2443
## All.X.lm 96.59996 0.5993746699 134.7805
## All.X.glm 96.59996 0.5993746699 134.7805
## All.X.bayesglm 95.49461 0.6037971836 134.0346
## All.X.glmnet 89.83738 0.5792308893 138.1274
## All.X.no.rnorm.rpart 111.83847 0.4505450166 157.8425
## All.X.no.rnorm.rf 91.81702 0.6249481535 130.2892
## All.Interact.X.lm 94.75209 0.6027366015 134.2138
## All.Interact.X.glm 94.75209 0.6027366015 134.2138
## All.Interact.X.bayesglm 93.42035 0.6061055541 133.6435
## All.Interact.X.glmnet 88.78737 0.6094109698 133.0815
## All.Interact.X.no.rnorm.rpart 112.23418 0.3159370407 176.1188
## All.Interact.X.no.rnorm.rf 92.29990 0.6070976254 133.3578
## csm.lm 96.42778 0.5974058441 135.1112
## csm.glm 96.42778 0.5974058441 135.1112
## csm.bayesglm 95.51020 0.5989458091 134.8527
## csm.glmnet 91.69400 0.6021921418 134.3057
## csm.rpart 111.83847 0.4505450166 157.8425
## csm.rf 97.97158 0.6189451942 131.3387
## max.Adj.R.sq.fit max.Rsquared.fit
## MFO.lm -0.001093153 NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart NA 0.2750573
## Max.cor.Y.lm 0.454975555 0.4524550
## Interact.High.cor.Y.lm 0.463501759 0.4579098
## Low.cor.X.lm 0.575613356 0.5096123
## All.X.lm 0.578508269 0.4783328
## All.X.glm NA 0.4783328
## All.X.bayesglm NA 0.4865310
## All.X.glmnet NA 0.5330513
## All.X.no.rnorm.rpart NA 0.2750573
## All.X.no.rnorm.rf NA 0.5195214
## All.Interact.X.lm 0.605933384 0.5019222
## All.Interact.X.glm NA 0.5019222
## All.Interact.X.bayesglm NA 0.5114766
## All.Interact.X.glmnet NA 0.5451064
## All.Interact.X.no.rnorm.rpart NA 0.2702555
## All.Interact.X.no.rnorm.rf NA 0.5140052
## csm.lm 0.556393828 0.4781052
## csm.glm NA 0.4781052
## csm.bayesglm NA 0.4855296
## csm.glmnet NA 0.5142438
## csm.rpart NA 0.2750573
## csm.rf NA 0.4641526
## min.RMSESD.fit max.RsquaredSD.fit
## MFO.lm NA NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart 3.592112 0.04148092
## Max.cor.Y.lm 3.422758 0.04130826
## Interact.High.cor.Y.lm 3.733880 0.04250557
## Low.cor.X.lm 3.032874 0.03283800
## All.X.lm 1.772347 0.01936568
## All.X.glm 1.772347 0.01936568
## All.X.bayesglm 2.262606 0.02327135
## All.X.glmnet 4.172042 0.04997399
## All.X.no.rnorm.rpart 3.592112 0.04148092
## All.X.no.rnorm.rf 5.812860 0.05553600
## All.Interact.X.lm 2.819659 0.01481398
## All.Interact.X.glm 2.819659 0.01481398
## All.Interact.X.bayesglm 2.646200 0.01574892
## All.Interact.X.glmnet 4.086101 0.04518189
## All.Interact.X.no.rnorm.rpart 3.565753 0.04046247
## All.Interact.X.no.rnorm.rf 5.011737 0.04418812
## csm.lm 8.600697 0.07746978
## csm.glm 8.600697 0.07746978
## csm.bayesglm 8.673136 0.08078789
## csm.glmnet 8.875314 0.09451454
## csm.rpart 3.592112 0.04148092
## csm.rf 11.787921 0.11218487
## min.aic.fit
## MFO.lm NA
## Max.cor.Y.cv.0.rpart NA
## Max.cor.Y.cv.0.cp.0.rpart NA
## Max.cor.Y.rpart NA
## Max.cor.Y.lm NA
## Interact.High.cor.Y.lm NA
## Low.cor.X.lm NA
## All.X.lm NA
## All.X.glm 10160.73
## All.X.bayesglm 10182.24
## All.X.glmnet NA
## All.X.no.rnorm.rpart NA
## All.X.no.rnorm.rf NA
## All.Interact.X.lm NA
## All.Interact.X.glm 10113.57
## All.Interact.X.bayesglm 10134.83
## All.Interact.X.glmnet NA
## All.Interact.X.no.rnorm.rpart NA
## All.Interact.X.no.rnorm.rf NA
## csm.lm NA
## csm.glm 10214.53
## csm.bayesglm 10232.62
## csm.glmnet NA
## csm.rpart NA
## csm.rf NArm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models_1_rf 13 0 176.320 246.093 69.773
## 14 fit.models_1_end 14 0 246.093 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 114.634 246.1 131.466
## 12 fit.models 7 2 246.100 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id",
grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df),
grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## MFO.lm MFO.lm lm
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.lm Max.cor.Y.lm lm
## Interact.High.cor.Y.lm Interact.High.cor.Y.lm lm
## Low.cor.X.lm Low.cor.X.lm lm
## All.X.lm All.X.lm lm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.glmnet All.X.glmnet glmnet
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## All.Interact.X.lm All.Interact.X.lm lm
## All.Interact.X.glm All.Interact.X.glm glm
## All.Interact.X.bayesglm All.Interact.X.bayesglm bayesglm
## All.Interact.X.glmnet All.Interact.X.glmnet glmnet
## All.Interact.X.no.rnorm.rpart All.Interact.X.no.rnorm.rpart rpart
## All.Interact.X.no.rnorm.rf All.Interact.X.no.rnorm.rf rf
## csm.lm csm.lm lm
## csm.glm csm.glm glm
## csm.bayesglm csm.bayesglm bayesglm
## csm.glmnet csm.glmnet glmnet
## csm.rpart csm.rpart rpart
## csm.rf csm.rf rf
## feats
## MFO.lm .rnorm
## Max.cor.Y.cv.0.rpart biddable, prdline.my.fctr
## Max.cor.Y.cv.0.cp.0.rpart biddable, prdline.my.fctr
## Max.cor.Y.rpart biddable, prdline.my.fctr
## Max.cor.Y.lm biddable, prdline.my.fctr
## Interact.High.cor.Y.lm biddable, prdline.my.fctr, biddable:D.TfIdf.sum.post.stop, biddable:D.npnct06.log, biddable:D.npnct03.log, biddable:D.terms.n.post.stem, biddable:D.nuppr.log, biddable:D.nwrds.unq.log, biddable:D.npnct24.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.TfIdf.sum.post.stem
## Low.cor.X.lm prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct12.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.lm prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.glm prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.bayesglm prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.glmnet prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rpart prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rf prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.lm prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glm prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.bayesglm prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glmnet prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, .rnorm, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rpart prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rf prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## csm.lm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.glm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.bayesglm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.glmnet prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.rpart prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.rf prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns max.R.sq.fit max.R.sq.OOB
## MFO.lm 0 7.226357e-05 0.0001316983
## Max.cor.Y.cv.0.rpart 0 0.000000e+00 0.0000000000
## Max.cor.Y.cv.0.cp.0.rpart 0 4.923724e-01 0.5489639195
## Max.cor.Y.rpart 3 3.121279e-01 0.4505450166
## Max.cor.Y.lm 1 4.594170e-01 0.5186350614
## Interact.High.cor.Y.lm 1 4.734947e-01 0.5214588759
## Low.cor.X.lm 1 6.072324e-01 0.6201210933
## All.X.lm 1 6.162904e-01 0.5993746699
## All.X.glm 1 6.162904e-01 0.5993746699
## All.X.bayesglm 1 6.156165e-01 0.6037971836
## All.X.glmnet 9 5.836410e-01 0.5792308893
## All.X.no.rnorm.rpart 3 3.121279e-01 0.4505450166
## All.X.no.rnorm.rf 3 8.931938e-01 0.6249481535
## All.Interact.X.lm 1 6.467622e-01 0.6027366015
## All.Interact.X.glm 1 6.467622e-01 0.6027366015
## All.Interact.X.bayesglm 1 6.462436e-01 0.6061055541
## All.Interact.X.glmnet 9 6.154744e-01 0.6094109698
## All.Interact.X.no.rnorm.rpart 3 2.294110e-01 0.3159370407
## All.Interact.X.no.rnorm.rf 3 8.966000e-01 0.6070976254
## csm.lm 1 6.018389e-01 0.5974058441
## csm.glm 1 6.018389e-01 0.5974058441
## csm.bayesglm 1 6.017966e-01 0.5989458091
## csm.glmnet 9 5.891504e-01 0.6021921418
## csm.rpart 3 3.121279e-01 0.4505450166
## csm.rf 3 6.913385e-01 0.6189451942
## max.Adj.R.sq.fit max.Rsquared.fit
## MFO.lm -0.001093153 NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart NA 0.2750573
## Max.cor.Y.lm 0.454975555 0.4524550
## Interact.High.cor.Y.lm 0.463501759 0.4579098
## Low.cor.X.lm 0.575613356 0.5096123
## All.X.lm 0.578508269 0.4783328
## All.X.glm NA 0.4783328
## All.X.bayesglm NA 0.4865310
## All.X.glmnet NA 0.5330513
## All.X.no.rnorm.rpart NA 0.2750573
## All.X.no.rnorm.rf NA 0.5195214
## All.Interact.X.lm 0.605933384 0.5019222
## All.Interact.X.glm NA 0.5019222
## All.Interact.X.bayesglm NA 0.5114766
## All.Interact.X.glmnet NA 0.5451064
## All.Interact.X.no.rnorm.rpart NA 0.2702555
## All.Interact.X.no.rnorm.rf NA 0.5140052
## csm.lm 0.556393828 0.4781052
## csm.glm NA 0.4781052
## csm.bayesglm NA 0.4855296
## csm.glmnet NA 0.5142438
## csm.rpart NA 0.2750573
## csm.rf NA 0.4641526
## inv.elapsedtime.everything
## MFO.lm 1.94931774
## Max.cor.Y.cv.0.rpart 1.54083205
## Max.cor.Y.cv.0.cp.0.rpart 2.09205021
## Max.cor.Y.rpart 0.96993210
## Max.cor.Y.lm 1.04166667
## Interact.High.cor.Y.lm 1.03092784
## Low.cor.X.lm 0.89047195
## All.X.lm 0.84961767
## All.X.glm 0.93457944
## All.X.bayesglm 0.43140638
## All.X.glmnet 0.62853551
## All.X.no.rnorm.rpart 0.69396253
## All.X.no.rnorm.rf 0.04086804
## All.Interact.X.lm 0.86505190
## All.Interact.X.glm 0.83472454
## All.Interact.X.bayesglm 0.48923679
## All.Interact.X.glmnet 0.64850843
## All.Interact.X.no.rnorm.rpart 0.65876153
## All.Interact.X.no.rnorm.rf 0.03676606
## csm.lm 0.88261253
## csm.glm 0.82101806
## csm.bayesglm 0.56148231
## csm.glmnet 0.74906367
## csm.rpart 0.65703022
## csm.rf 0.04444642
## inv.elapsedtime.final inv.RMSE.fit
## MFO.lm 333.3333333 0.007631261
## Max.cor.Y.cv.0.rpart 66.6666667 0.007630985
## Max.cor.Y.cv.0.cp.0.rpart 111.1111111 0.010710456
## Max.cor.Y.rpart 83.3333333 0.008941467
## Max.cor.Y.lm 200.0000000 0.010295595
## Interact.High.cor.Y.lm 111.1111111 0.010348794
## Low.cor.X.lm 31.2500000 0.010805378
## All.X.lm 25.0000000 0.010351971
## All.X.glm 17.5438596 0.010351971
## All.X.bayesglm 2.5974026 0.010471795
## All.X.glmnet 21.2765957 0.011131225
## All.X.no.rnorm.rpart 16.9491525 0.008941467
## All.X.no.rnorm.rf 0.1181893 0.010891227
## All.Interact.X.lm 22.2222222 0.010553857
## All.Interact.X.glm 15.6250000 0.010553857
## All.Interact.X.bayesglm 2.1551724 0.010704305
## All.Interact.X.glmnet 16.3934426 0.011262863
## All.Interact.X.no.rnorm.rpart 15.1515152 0.008909941
## All.Interact.X.no.rnorm.rf 0.1059771 0.010834249
## csm.lm 21.7391304 0.010370456
## csm.glm 15.6250000 0.010370456
## csm.bayesglm 3.0487805 0.010470086
## csm.glmnet 37.0370370 0.010905839
## csm.rpart 15.8730159 0.008941467
## csm.rf 0.1312853 0.010207041
## inv.RMSE.OOB inv.aic.fit
## MFO.lm 0.004696462 NA
## Max.cor.Y.cv.0.rpart 0.004696153 NA
## Max.cor.Y.cv.0.cp.0.rpart 0.006992566 NA
## Max.cor.Y.rpart 0.006335431 NA
## Max.cor.Y.lm 0.006768696 NA
## Interact.High.cor.Y.lm 0.006788637 NA
## Low.cor.X.lm 0.007619380 NA
## All.X.lm 0.007419473 NA
## All.X.glm 0.007419473 9.841810e-05
## All.X.bayesglm 0.007460761 9.821020e-05
## All.X.glmnet 0.007239696 NA
## All.X.no.rnorm.rpart 0.006335431 NA
## All.X.no.rnorm.rf 0.007675233 NA
## All.Interact.X.lm 0.007450801 NA
## All.Interact.X.glm 0.007450801 9.887703e-05
## All.Interact.X.bayesglm 0.007482591 9.866960e-05
## All.Interact.X.glmnet 0.007514191 NA
## All.Interact.X.no.rnorm.rpart 0.005677985 NA
## All.Interact.X.no.rnorm.rf 0.007498622 NA
## csm.lm 0.007401309 NA
## csm.glm 0.007401309 9.789978e-05
## csm.bayesglm 0.007415498 9.772669e-05
## csm.glmnet 0.007445701 NA
## csm.rpart 0.006335431 NA
## csm.rf 0.007613905 NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 25. Consider specifying shapes manually if you must have them.## Warning: Removed 5 rows containing missing values (geom_path).## Warning: Removed 203 rows containing missing values (geom_point).## Warning: Removed 40 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 25. Consider specifying shapes manually if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## [1] "var:min.RMSESD.fit"
## [1] "var:max.RsquaredSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 3 rows containing missing values (position_stack).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 3 rows containing missing values (position_stack).
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
dsp_models_cols <- c("model_id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])## model_id min.RMSE.OOB max.R.sq.OOB
## 13 All.X.no.rnorm.rf 130.2892 0.6249481535
## 7 Low.cor.X.lm 131.2443 0.6201210933
## 25 csm.rf 131.3387 0.6189451942
## 17 All.Interact.X.glmnet 133.0815 0.6094109698
## 19 All.Interact.X.no.rnorm.rf 133.3578 0.6070976254
## 16 All.Interact.X.bayesglm 133.6435 0.6061055541
## 10 All.X.bayesglm 134.0346 0.6037971836
## 14 All.Interact.X.lm 134.2138 0.6027366015
## 15 All.Interact.X.glm 134.2138 0.6027366015
## 23 csm.glmnet 134.3057 0.6021921418
## 9 All.X.glm 134.7805 0.5993746699
## 8 All.X.lm 134.7805 0.5993746699
## 22 csm.bayesglm 134.8527 0.5989458091
## 21 csm.glm 135.1112 0.5974058441
## 20 csm.lm 135.1112 0.5974058441
## 11 All.X.glmnet 138.1274 0.5792308893
## 3 Max.cor.Y.cv.0.cp.0.rpart 143.0090 0.5489639195
## 6 Interact.High.cor.Y.lm 147.3050 0.5214588759
## 5 Max.cor.Y.lm 147.7389 0.5186350614
## 12 All.X.no.rnorm.rpart 157.8425 0.4505450166
## 24 csm.rpart 157.8425 0.4505450166
## 4 Max.cor.Y.rpart 157.8425 0.4505450166
## 18 All.Interact.X.no.rnorm.rpart 176.1188 0.3159370407
## 1 MFO.lm 212.9262 0.0001316983
## 2 Max.cor.Y.cv.0.rpart 212.9402 0.0000000000
## max.Adj.R.sq.fit
## 13 NA
## 7 0.575613356
## 25 NA
## 17 NA
## 19 NA
## 16 NA
## 10 NA
## 14 0.605933384
## 15 NA
## 23 NA
## 9 NA
## 8 0.578508269
## 22 NA
## 21 NA
## 20 0.556393828
## 11 NA
## 3 NA
## 6 0.463501759
## 5 0.454975555
## 12 NA
## 24 NA
## 4 NA
## 18 NA
## 1 -0.001093153
## 2 NAprint(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 25. Consider specifying shapes manually if you must have them.## Warning: Removed 8 rows containing missing values (geom_path).## Warning: Removed 68 rows containing missing values (geom_point).## Warning: Removed 18 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 25. Consider specifying shapes manually if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~+min.RMSE.OOB - max.R.sq.OOB - max.Adj.R.sq.fitprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: All.X.no.rnorm.rf"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
# if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
# warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
# glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
# }
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 860 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 860 -none- numeric
## importance 86 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 860 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 86 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")
}
return(df)
}
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## UniqueID
## 2623 12625
## 1396 11397
## 1418 11419
## 2501 12503
## 2507 12509
## 1487 11488
## description
## 2623 Lot of 10 mixed iPad minis. Colors,models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 2507
## 1487
## biddable startprice condition cellular carrier
## 2623 0 999.99 For parts or not working Unknown Unknown
## 1396 0 999.00 Used 0 None
## 1418 1 700.00 Used Unknown Unknown
## 2501 1 879.99 New 0 None
## 2507 1 729.99 New 0 None
## 1487 1 729.99 New 0 None
## color storage productline .src .grpid .rnorm idseq.my
## 2623 White Unknown Unknown Test <NA> -0.9259777 2625
## 1396 Unknown 32 iPad mini Test <NA> -0.1429904 1397
## 1418 Unknown Unknown Unknown Test <NA> 0.7258252 1419
## 2501 Space Gray 128 iPad Air 2 Test <NA> 1.7466852 2503
## 2507 Gold 128 iPad mini 3 Test <NA> 0.8371948 2509
## 1487 Space Gray 128 iPad mini 3 Test <NA> 0.7174519 1488
## prdline.my startprice.log
## 2623 iPadmini 6.907745
## 1396 iPadmini 6.906755
## 1418 Unknown 6.551080
## 2501 iPadAir 6.779911
## 2507 iPadmini 2+ 6.593031
## 1487 iPadmini 2+ 6.593031
## descr.my
## 2623 Lot of 10 mixed iPad minis. Colors, models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 2507
## 1487
## condition.fctr cellular.fctr carrier.fctr color.fctr
## 2623 For parts or not working Unknown Unknown White
## 1396 Used 0 None Unknown
## 1418 Used Unknown Unknown Unknown
## 2501 New 0 None Space Gray
## 2507 New 0 None Gold
## 1487 New 0 None Space Gray
## storage.fctr prdline.my.fctr D.terms.n.post.stop
## 2623 Unknown iPadmini 8
## 1396 32 iPadmini 0
## 1418 Unknown Unknown 0
## 2501 128 iPadAir 0
## 2507 128 iPadmini 2+ 0
## 1487 128 iPadmini 2+ 0
## D.terms.n.post.stop.log D.TfIdf.sum.post.stop D.terms.n.post.stem
## 2623 2.197225 9.127623 8
## 1396 0.000000 0.000000 0
## 1418 0.000000 0.000000 0
## 2501 0.000000 0.000000 0
## 2507 0.000000 0.000000 0
## 1487 0.000000 0.000000 0
## D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 2623 2.197225 8.069403
## 1396 0.000000 0.000000
## 1418 0.000000 0.000000
## 2501 0.000000 0.000000
## 2507 0.000000 0.000000
## 1487 0.000000 0.000000
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.condit
## 2623 1 0.884064 0
## 1396 1 1.000000 0
## 1418 1 1.000000 0
## 2501 1 1.000000 0
## 2507 1 1.000000 0
## 1487 1 1.000000 0
## D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen D.T.great
## 2623 0 0 0 0 0.3908446 0 0
## 1396 0 0 0 0 0.0000000 0 0
## 1418 0 0 0 0 0.0000000 0 0
## 2501 0 0 0 0 0.0000000 0 0
## 2507 0 0 0 0 0.0000000 0 0
## 1487 0 0 0 0 0.0000000 0 0
## D.T.work D.T.excel D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 2623 0 0 2.944439 2.197225 8.069403
## 1396 0 0 0.000000 0.000000 0.000000
## 1418 0 0 0.000000 0.000000 0.000000
## 2501 0 0 0.000000 0.000000 0.000000
## 2507 0 0 0.000000 0.000000 0.000000
## 1487 0 0 0.000000 0.000000 0.000000
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log D.ndgts.log
## 2623 0.4483002 4.634729 4.356709 1.098612
## 1396 0.0000000 0.000000 0.000000 0.000000
## 1418 0.0000000 0.000000 0.000000 0.000000
## 2501 0.0000000 0.000000 0.000000 0.000000
## 2507 0.0000000 0.000000 0.000000 0.000000
## 1487 0.0000000 0.000000 0.000000 0.000000
## D.npnct01.log D.npnct03.log D.npnct05.log D.npnct06.log D.npnct08.log
## 2623 0 0 0 0.6931472 0
## 1396 0 0 0 0.0000000 0
## 1418 0 0 0 0.0000000 0
## 2501 0 0 0 0.0000000 0
## 2507 0 0 0 0.0000000 0
## 1487 0 0 0 0.0000000 0
## D.npnct11.log D.npnct12.log D.npnct13.log D.npnct14.log D.npnct15.log
## 2623 0.6931472 0 1.098612 0 0
## 1396 0.0000000 0 0.000000 0 0
## 1418 0.0000000 0 0.000000 0 0
## 2501 0.0000000 0 0.000000 0 0
## 2507 0.0000000 0 0.000000 0 0
## 1487 0.0000000 0 0.000000 0 0
## D.npnct16.log D.npnct24.log D.nstopwrds.log D.ratio.nstopwrds.nwrds
## 2623 0.6931472 0.6931472 2.197225 0.4736842
## 1396 0.0000000 0.0000000 0.000000 1.0000000
## 1418 0.0000000 0.0000000 0.000000 1.0000000
## 2501 0.0000000 0.0000000 0.000000 1.0000000
## 2507 0.0000000 0.0000000 0.000000 1.0000000
## 1487 0.0000000 0.0000000 0.000000 1.0000000
## D.P.mini D.P.air .clusterid .clusterid.fctr
## 2623 1 0 4 4
## 1396 0 0 1 1
## 1418 0 0 1 1
## 2501 0 0 1 1
## 2507 0 0 1 1
## 1487 0 0 1 1
## startprice.predict.All.X.no.rnorm.rf
## 2623 152.91255
## 1396 208.42139
## 1418 77.88643
## 2501 258.00443
## 2507 122.67522
## 1487 133.23766
## startprice.predict.All.X.no.rnorm.rf.err
## 2623 847.0774
## 1396 790.5786
## 1418 622.1136
## 2501 621.9856
## 2507 607.3148
## 1487 596.7523predct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
predct_error_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".err")
glb_OOBobs_df[, predct_accurate_var_name] <-
(glb_OOBobs_df[, glb_rsp_var] ==
glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## importance
## biddable 1.000000e+02
## prdline.my.fctriPadAir 6.017061e+01
## idseq.my 5.834247e+01
## condition.fctrNew 3.187377e+01
## prdline.my.fctriPadmini 2+ 1.584214e+01
## prdline.my.fctriPad 1 1.269167e+01
## storage.fctr16 1.111043e+01
## condition.fctrFor parts or not working 9.445925e+00
## storage.fctr64 8.479953e+00
## D.TfIdf.sum.stem.stop.Ratio 7.845998e+00
## color.fctrWhite 6.841606e+00
## D.ratio.sum.TfIdf.nwrds 6.639130e+00
## color.fctrGold 6.526682e+00
## cellular.fctr1 6.328521e+00
## D.ratio.nstopwrds.nwrds 6.086762e+00
## carrier.fctrUnknown 5.477986e+00
## color.fctrSpace Gray 5.299191e+00
## condition.fctrNew other (see details) 4.690868e+00
## prdline.my.fctriPad 3+ 4.038812e+00
## color.fctrBlack 3.712488e+00
## storage.fctr32 3.553819e+00
## carrier.fctrAT&T 3.424452e+00
## D.TfIdf.sum.post.stop 3.185491e+00
## cellular.fctrUnknown 3.045839e+00
## carrier.fctrVerizon 2.871059e+00
## storage.fctrUnknown 2.855441e+00
## D.sum.TfIdf 2.633770e+00
## D.TfIdf.sum.post.stem 2.581885e+00
## D.nuppr.log 2.449205e+00
## prdline.my.fctriPad 2 2.390203e+00
## prdline.my.fctrUnknown:.clusterid.fctr2 1.933986e+00
## prdline.my.fctriPadmini 1.785961e+00
## carrier.fctrSprint 1.755063e+00
## D.nchrs.log 1.717401e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 1.612254e+00
## D.nstopwrds.log 1.601153e+00
## D.ndgts.log 1.499496e+00
## D.npnct13.log 1.470214e+00
## D.nwrds.log 1.414854e+00
## D.npnct11.log 1.354603e+00
## carrier.fctrT-Mobile 1.040226e+00
## D.terms.n.post.stem 9.785629e-01
## D.terms.n.post.stop 9.386855e-01
## D.terms.n.post.stem.log 9.136787e-01
## D.nwrds.unq.log 8.807968e-01
## D.terms.n.post.stop.log 8.766611e-01
## D.npnct01.log 8.763425e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 7.410079e-01
## condition.fctrManufacturer refurbished 7.341349e-01
## condition.fctrSeller refurbished 7.139955e-01
## D.terms.n.stem.stop.Ratio 4.135959e-01
## carrier.fctrOther 3.500180e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 2.412510e-01
## D.npnct15.log 2.138585e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 2.006444e-01
## D.npnct16.log 1.985543e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 1.963476e-01
## prdline.my.fctriPadAir:.clusterid.fctr4 1.913993e-01
## D.npnct06.log 1.899101e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.896488e-01
## D.npnct12.log 1.830369e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.582730e-01
## D.npnct24.log 1.582566e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 1.462750e-01
## D.npnct14.log 1.302889e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 1.257171e-01
## D.npnct08.log 1.139584e-01
## D.npnct03.log 1.084861e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 9.910833e-02
## prdline.my.fctriPad 2:.clusterid.fctr2 8.053090e-02
## prdline.my.fctriPad 2:.clusterid.fctr4 7.364192e-02
## prdline.my.fctriPad 1:.clusterid.fctr2 3.848913e-02
## prdline.my.fctriPad 2:.clusterid.fctr3 3.822434e-02
## prdline.my.fctriPad 1:.clusterid.fctr3 3.177592e-02
## prdline.my.fctriPad 2:.clusterid.fctr5 3.162311e-02
## prdline.my.fctrUnknown:.clusterid.fctr3 2.518860e-02
## D.npnct05.log 2.124534e-02
## prdline.my.fctriPad 1:.clusterid.fctr4 1.233498e-02
## prdline.my.fctriPadmini:.clusterid.fctr5 8.680101e-03
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 3+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## All.X.no.rnorm.rf.importance
## biddable 1.000000e+02
## prdline.my.fctriPadAir 6.017061e+01
## idseq.my 5.834247e+01
## condition.fctrNew 3.187377e+01
## prdline.my.fctriPadmini 2+ 1.584214e+01
## prdline.my.fctriPad 1 1.269167e+01
## storage.fctr16 1.111043e+01
## condition.fctrFor parts or not working 9.445925e+00
## storage.fctr64 8.479953e+00
## D.TfIdf.sum.stem.stop.Ratio 7.845998e+00
## color.fctrWhite 6.841606e+00
## D.ratio.sum.TfIdf.nwrds 6.639130e+00
## color.fctrGold 6.526682e+00
## cellular.fctr1 6.328521e+00
## D.ratio.nstopwrds.nwrds 6.086762e+00
## carrier.fctrUnknown 5.477986e+00
## color.fctrSpace Gray 5.299191e+00
## condition.fctrNew other (see details) 4.690868e+00
## prdline.my.fctriPad 3+ 4.038812e+00
## color.fctrBlack 3.712488e+00
## storage.fctr32 3.553819e+00
## carrier.fctrAT&T 3.424452e+00
## D.TfIdf.sum.post.stop 3.185491e+00
## cellular.fctrUnknown 3.045839e+00
## carrier.fctrVerizon 2.871059e+00
## storage.fctrUnknown 2.855441e+00
## D.sum.TfIdf 2.633770e+00
## D.TfIdf.sum.post.stem 2.581885e+00
## D.nuppr.log 2.449205e+00
## prdline.my.fctriPad 2 2.390203e+00
## prdline.my.fctrUnknown:.clusterid.fctr2 1.933986e+00
## prdline.my.fctriPadmini 1.785961e+00
## carrier.fctrSprint 1.755063e+00
## D.nchrs.log 1.717401e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 1.612254e+00
## D.nstopwrds.log 1.601153e+00
## D.ndgts.log 1.499496e+00
## D.npnct13.log 1.470214e+00
## D.nwrds.log 1.414854e+00
## D.npnct11.log 1.354603e+00
## carrier.fctrT-Mobile 1.040226e+00
## D.terms.n.post.stem 9.785629e-01
## D.terms.n.post.stop 9.386855e-01
## D.terms.n.post.stem.log 9.136787e-01
## D.nwrds.unq.log 8.807968e-01
## D.terms.n.post.stop.log 8.766611e-01
## D.npnct01.log 8.763425e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 7.410079e-01
## condition.fctrManufacturer refurbished 7.341349e-01
## condition.fctrSeller refurbished 7.139955e-01
## D.terms.n.stem.stop.Ratio 4.135959e-01
## carrier.fctrOther 3.500180e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 2.412510e-01
## D.npnct15.log 2.138585e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 2.006444e-01
## D.npnct16.log 1.985543e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 1.963476e-01
## prdline.my.fctriPadAir:.clusterid.fctr4 1.913993e-01
## D.npnct06.log 1.899101e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.896488e-01
## D.npnct12.log 1.830369e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.582730e-01
## D.npnct24.log 1.582566e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 1.462750e-01
## D.npnct14.log 1.302889e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 1.257171e-01
## D.npnct08.log 1.139584e-01
## D.npnct03.log 1.084861e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 9.910833e-02
## prdline.my.fctriPad 2:.clusterid.fctr2 8.053090e-02
## prdline.my.fctriPad 2:.clusterid.fctr4 7.364192e-02
## prdline.my.fctriPad 1:.clusterid.fctr2 3.848913e-02
## prdline.my.fctriPad 2:.clusterid.fctr3 3.822434e-02
## prdline.my.fctriPad 1:.clusterid.fctr3 3.177592e-02
## prdline.my.fctriPad 2:.clusterid.fctr5 3.162311e-02
## prdline.my.fctrUnknown:.clusterid.fctr3 2.518860e-02
## D.npnct05.log 2.124534e-02
## prdline.my.fctriPad 1:.clusterid.fctr4 1.233498e-02
## prdline.my.fctriPadmini:.clusterid.fctr5 8.680101e-03
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 3+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
featsimp_df <- glb_featsimp_df
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
if (nrow(featsimp_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(featsimp_df) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id): Limiting important feature scatter plots to 5 out of 38
## UniqueID
## 2623 12625
## 1396 11397
## 1418 11419
## 2501 12503
## 2507 12509
## description
## 2623 Lot of 10 mixed iPad minis. Colors,models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 2507
## biddable startprice condition cellular carrier
## 2623 0 999.99 For parts or not working Unknown Unknown
## 1396 0 999.00 Used 0 None
## 1418 1 700.00 Used Unknown Unknown
## 2501 1 879.99 New 0 None
## 2507 1 729.99 New 0 None
## color storage productline .src .grpid .rnorm idseq.my
## 2623 White Unknown Unknown Test <NA> -0.9259777 2625
## 1396 Unknown 32 iPad mini Test <NA> -0.1429904 1397
## 1418 Unknown Unknown Unknown Test <NA> 0.7258252 1419
## 2501 Space Gray 128 iPad Air 2 Test <NA> 1.7466852 2503
## 2507 Gold 128 iPad mini 3 Test <NA> 0.8371948 2509
## prdline.my startprice.log
## 2623 iPadmini 6.907745
## 1396 iPadmini 6.906755
## 1418 Unknown 6.551080
## 2501 iPadAir 6.779911
## 2507 iPadmini 2+ 6.593031
## descr.my
## 2623 Lot of 10 mixed iPad minis. Colors, models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 2507
## condition.fctr cellular.fctr carrier.fctr color.fctr
## 2623 For parts or not working Unknown Unknown White
## 1396 Used 0 None Unknown
## 1418 Used Unknown Unknown Unknown
## 2501 New 0 None Space Gray
## 2507 New 0 None Gold
## storage.fctr prdline.my.fctr D.terms.n.post.stop
## 2623 Unknown iPadmini 8
## 1396 32 iPadmini 0
## 1418 Unknown Unknown 0
## 2501 128 iPadAir 0
## 2507 128 iPadmini 2+ 0
## D.terms.n.post.stop.log D.TfIdf.sum.post.stop D.terms.n.post.stem
## 2623 2.197225 9.127623 8
## 1396 0.000000 0.000000 0
## 1418 0.000000 0.000000 0
## 2501 0.000000 0.000000 0
## 2507 0.000000 0.000000 0
## D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 2623 2.197225 8.069403
## 1396 0.000000 0.000000
## 1418 0.000000 0.000000
## 2501 0.000000 0.000000
## 2507 0.000000 0.000000
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.condit
## 2623 1 0.884064 0
## 1396 1 1.000000 0
## 1418 1 1.000000 0
## 2501 1 1.000000 0
## 2507 1 1.000000 0
## D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen D.T.great
## 2623 0 0 0 0 0.3908446 0 0
## 1396 0 0 0 0 0.0000000 0 0
## 1418 0 0 0 0 0.0000000 0 0
## 2501 0 0 0 0 0.0000000 0 0
## 2507 0 0 0 0 0.0000000 0 0
## D.T.work D.T.excel D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 2623 0 0 2.944439 2.197225 8.069403
## 1396 0 0 0.000000 0.000000 0.000000
## 1418 0 0 0.000000 0.000000 0.000000
## 2501 0 0 0.000000 0.000000 0.000000
## 2507 0 0 0.000000 0.000000 0.000000
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log D.ndgts.log
## 2623 0.4483002 4.634729 4.356709 1.098612
## 1396 0.0000000 0.000000 0.000000 0.000000
## 1418 0.0000000 0.000000 0.000000 0.000000
## 2501 0.0000000 0.000000 0.000000 0.000000
## 2507 0.0000000 0.000000 0.000000 0.000000
## D.npnct01.log D.npnct03.log D.npnct05.log D.npnct06.log D.npnct08.log
## 2623 0 0 0 0.6931472 0
## 1396 0 0 0 0.0000000 0
## 1418 0 0 0 0.0000000 0
## 2501 0 0 0 0.0000000 0
## 2507 0 0 0 0.0000000 0
## D.npnct11.log D.npnct12.log D.npnct13.log D.npnct14.log D.npnct15.log
## 2623 0.6931472 0 1.098612 0 0
## 1396 0.0000000 0 0.000000 0 0
## 1418 0.0000000 0 0.000000 0 0
## 2501 0.0000000 0 0.000000 0 0
## 2507 0.0000000 0 0.000000 0 0
## D.npnct16.log D.npnct24.log D.nstopwrds.log D.ratio.nstopwrds.nwrds
## 2623 0.6931472 0.6931472 2.197225 0.4736842
## 1396 0.0000000 0.0000000 0.000000 1.0000000
## 1418 0.0000000 0.0000000 0.000000 1.0000000
## 2501 0.0000000 0.0000000 0.000000 1.0000000
## 2507 0.0000000 0.0000000 0.000000 1.0000000
## D.P.mini D.P.air .clusterid .clusterid.fctr
## 2623 1 0 4 4
## 1396 0 0 1 1
## 1418 0 0 1 1
## 2501 0 0 1 1
## 2507 0 0 1 1
## startprice.predict.All.X.no.rnorm.rf
## 2623 152.91255
## 1396 208.42139
## 1418 77.88643
## 2501 258.00443
## 2507 122.67522
## startprice.predict.All.X.no.rnorm.rf.err
## 2623 847.0774
## 1396 790.5786
## 1418 622.1136
## 2501 621.9856
## 2507 607.3148
## startprice.predict.All.X.no.rnorm.rf.accurate .label
## 2623 FALSE 12625
## 1396 FALSE 11397
## 1418 FALSE 11419
## 2501 FALSE 12503
## 2507 FALSE 12509
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBobs_df[, glb_rsp_var])$table))
# FN_OOB_ids <- c(4721, 4020, 693, 92)
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_feats_df$id[1:5]])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
# write.csv(glb_chunks_df, paste0(glb_out_pfx, tail(glb_chunks_df, 1)$label, "_",
# tail(glb_chunks_df, 1)$step_minor, "_chunks1.csv"),
# row.names=FALSE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 12 fit.models 7 2 246.100 269.152 23.052
## 13 fit.models 7 3 269.153 NA NA if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
stop("fit.models_3: Why is this happening ?")## Warning in is.na(glb_allobs_df$D.P.http): is.na() applied to non-(list or
## vector) of type 'NULL'#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## [1] "startprice.predict.All.X.no.rnorm.rf"
## [2] "startprice.predict.All.X.no.rnorm.rf.err"
## [3] "startprice.predict.All.X.no.rnorm.rf.accurate"for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models 7 3 269.153 276.619 7.466
## 14 fit.data.training 8 0 276.620 NA NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
stop("fit.data.training_0: Why is this happening ?")## Warning in is.na(glb_allobs_df$D.P.http): is.na() applied to non-(list or
## vector) of type 'NULL'# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
# print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
# entity_df=glb_fitobs_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
require(gdata)
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=trim(unlist(strsplit(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## [1] "fitting model: Final.rf"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 860 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 860 -none- numeric
## importance 86 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 860 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 86 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## model_id model_method
## 1 Final.rf rf
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, carrier.fctr, storage.fctr, D.npnct14.log, D.terms.n.stem.stop.Ratio, cellular.fctr, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 14.732 8.507
## max.R.sq.fit min.RMSE.fit max.Rsquared.fit min.RMSESD.fit
## 1 0.8931938 91.7543 0.5198255 5.935772
## max.RsquaredSD.fit
## 1 0.05778122rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 8 0 276.620 292.946 16.326
## 15 fit.data.training 8 1 292.947 NA NA#```
#```{r fit.data.training_1, cache=FALSE}
glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## UniqueID
## 1358 11359
## 1299 11300
## 1704 11705
## 1447 11448
## 792 10792
## 794 10794
## description
## 1358
## 1299
## 1704
## 1447
## 792
## 794 Excellent Used Condition. Please see all photos and description.
## biddable startprice condition cellular carrier color storage
## 1358 0 595.00 New Unknown Unknown Unknown Unknown
## 1299 1 650.00 New 1 Unknown Gold 128
## 1704 0 590.00 New Unknown Unknown White 16
## 1447 0 4.69 New Unknown Unknown Unknown Unknown
## 792 1 550.00 Used 0 None Gold 128
## 794 1 525.00 Used 0 None White 128
## productline .src .grpid .rnorm idseq.my prdline.my
## 1358 Unknown Train <NA> -0.3914823 1359 Unknown
## 1299 iPad Air 2 Train <NA> 1.3370014 1300 iPadAir
## 1704 Unknown Train <NA> -0.9839429 1705 Unknown
## 1447 Unknown Train <NA> -0.7916194 1448 Unknown
## 792 iPad Air 2 Train <NA> -0.2098595 792 iPadAir
## 794 iPad Air 2 Train <NA> -2.3482830 794 iPadAir
## startprice.log
## 1358 6.388561
## 1299 6.476972
## 1704 6.380123
## 1447 1.545433
## 792 6.309918
## 794 6.263398
## descr.my
## 1358
## 1299
## 1704
## 1447
## 792
## 794 Excellent Used Condition. Please see all photos and description.
## condition.fctr cellular.fctr carrier.fctr color.fctr storage.fctr
## 1358 New Unknown Unknown Unknown Unknown
## 1299 New 1 Unknown Gold 128
## 1704 New Unknown Unknown White 16
## 1447 New Unknown Unknown Unknown Unknown
## 792 Used 0 None Gold 128
## 794 Used 0 None White 128
## prdline.my.fctr D.terms.n.post.stop D.terms.n.post.stop.log
## 1358 Unknown 0 0.000000
## 1299 iPadAir 0 0.000000
## 1704 Unknown 0 0.000000
## 1447 Unknown 0 0.000000
## 792 iPadAir 0 0.000000
## 794 iPadAir 7 2.079442
## D.TfIdf.sum.post.stop D.terms.n.post.stem D.terms.n.post.stem.log
## 1358 0.000000 0 0.000000
## 1299 0.000000 0 0.000000
## 1704 0.000000 0 0.000000
## 1447 0.000000 0 0.000000
## 792 0.000000 0 0.000000
## 794 4.983944 7 2.079442
## D.TfIdf.sum.post.stem D.terms.n.stem.stop.Ratio
## 1358 0.000000 1
## 1299 0.000000 1
## 1704 0.000000 1
## 1447 0.000000 1
## 792 0.000000 1
## 794 4.926312 1
## D.TfIdf.sum.stem.stop.Ratio D.T.condit D.T.use D.T.scratch D.T.new
## 1358 1.0000000 0.0000000 0.0000000 0 0
## 1299 1.0000000 0.0000000 0.0000000 0 0
## 1704 1.0000000 0.0000000 0.0000000 0 0
## 1447 1.0000000 0.0000000 0.0000000 0 0
## 792 1.0000000 0.0000000 0.0000000 0 0
## 794 0.9884365 0.3459123 0.4558153 0 0
## D.T.good D.T.ipad D.T.screen D.T.great D.T.work D.T.excel D.nwrds.log
## 1358 0 0 0 0 0 0.0000000 0.000000
## 1299 0 0 0 0 0 0.0000000 0.000000
## 1704 0 0 0 0 0 0.0000000 0.000000
## 1447 0 0 0 0 0 0.0000000 0.000000
## 792 0 0 0 0 0 0.0000000 0.000000
## 794 0 0 0 0 0 0.6759609 2.302585
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds D.nchrs.log
## 1358 0.000000 0.000000 0.000000 0.000000
## 1299 0.000000 0.000000 0.000000 0.000000
## 1704 0.000000 0.000000 0.000000 0.000000
## 1447 0.000000 0.000000 0.000000 0.000000
## 792 0.000000 0.000000 0.000000 0.000000
## 794 2.079442 4.926312 0.547368 4.174387
## D.nuppr.log D.ndgts.log D.npnct01.log D.npnct03.log D.npnct05.log
## 1358 0.000000 0 0 0 0
## 1299 0.000000 0 0 0 0
## 1704 0.000000 0 0 0 0
## 1447 0.000000 0 0 0 0
## 792 0.000000 0 0 0 0
## 794 4.007333 0 0 0 0
## D.npnct06.log D.npnct08.log D.npnct11.log D.npnct12.log D.npnct13.log
## 1358 0 0 0 0 0.000000
## 1299 0 0 0 0 0.000000
## 1704 0 0 0 0 0.000000
## 1447 0 0 0 0 0.000000
## 792 0 0 0 0 0.000000
## 794 0 0 0 0 1.098612
## D.npnct14.log D.npnct15.log D.npnct16.log D.npnct24.log
## 1358 0 0 0 0.0000000
## 1299 0 0 0 0.0000000
## 1704 0 0 0 0.0000000
## 1447 0 0 0 0.0000000
## 792 0 0 0 0.0000000
## 794 0 0 0 0.6931472
## D.nstopwrds.log D.ratio.nstopwrds.nwrds D.P.mini D.P.air .clusterid
## 1358 0.000000 1.0 0 0 1
## 1299 0.000000 1.0 0 0 1
## 1704 0.000000 1.0 0 0 1
## 1447 0.000000 1.0 0 0 1
## 792 0.000000 1.0 0 0 1
## 794 1.098612 0.3 0 0 1
## .clusterid.fctr startprice.predict.Final.rf
## 1358 1 333.5124
## 1299 1 397.7206
## 1704 1 401.2191
## 1447 1 182.8442
## 792 1 377.7852
## 794 1 358.8398
## startprice.predict.Final.rf.err
## 1358 261.4876
## 1299 252.2794
## 1704 188.7809
## 1447 178.1542
## 792 172.2148
## 794 166.1602sav_featsimp_df <- glb_featsimp_df
#glb_feats_df <- sav_feats_df
# glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
# entity_df=glb_trnobs_df)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl, featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## All.X.no.rnorm.rf.importance
## biddable 1.000000e+02
## prdline.my.fctriPadAir 6.017061e+01
## idseq.my 5.834247e+01
## condition.fctrNew 3.187377e+01
## prdline.my.fctriPadmini 2+ 1.584214e+01
## prdline.my.fctriPad 1 1.269167e+01
## storage.fctr16 1.111043e+01
## condition.fctrFor parts or not working 9.445925e+00
## storage.fctr64 8.479953e+00
## D.TfIdf.sum.stem.stop.Ratio 7.845998e+00
## color.fctrWhite 6.841606e+00
## D.ratio.sum.TfIdf.nwrds 6.639130e+00
## color.fctrGold 6.526682e+00
## cellular.fctr1 6.328521e+00
## D.ratio.nstopwrds.nwrds 6.086762e+00
## carrier.fctrUnknown 5.477986e+00
## color.fctrSpace Gray 5.299191e+00
## condition.fctrNew other (see details) 4.690868e+00
## prdline.my.fctriPad 3+ 4.038812e+00
## color.fctrBlack 3.712488e+00
## storage.fctr32 3.553819e+00
## carrier.fctrAT&T 3.424452e+00
## D.TfIdf.sum.post.stop 3.185491e+00
## cellular.fctrUnknown 3.045839e+00
## carrier.fctrVerizon 2.871059e+00
## storage.fctrUnknown 2.855441e+00
## D.sum.TfIdf 2.633770e+00
## D.TfIdf.sum.post.stem 2.581885e+00
## D.nuppr.log 2.449205e+00
## prdline.my.fctriPad 2 2.390203e+00
## prdline.my.fctrUnknown:.clusterid.fctr2 1.933986e+00
## prdline.my.fctriPadmini 1.785961e+00
## carrier.fctrSprint 1.755063e+00
## D.nchrs.log 1.717401e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 1.612254e+00
## D.nstopwrds.log 1.601153e+00
## D.ndgts.log 1.499496e+00
## D.npnct13.log 1.470214e+00
## D.nwrds.log 1.414854e+00
## D.npnct11.log 1.354603e+00
## carrier.fctrT-Mobile 1.040226e+00
## D.terms.n.post.stem 9.785629e-01
## D.terms.n.post.stop 9.386855e-01
## D.terms.n.post.stem.log 9.136787e-01
## D.nwrds.unq.log 8.807968e-01
## D.terms.n.post.stop.log 8.766611e-01
## D.npnct01.log 8.763425e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 7.410079e-01
## condition.fctrManufacturer refurbished 7.341349e-01
## condition.fctrSeller refurbished 7.139955e-01
## D.terms.n.stem.stop.Ratio 4.135959e-01
## carrier.fctrOther 3.500180e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 2.412510e-01
## D.npnct15.log 2.138585e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 2.006444e-01
## D.npnct16.log 1.985543e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 1.963476e-01
## prdline.my.fctriPadAir:.clusterid.fctr4 1.913993e-01
## D.npnct06.log 1.899101e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.896488e-01
## D.npnct12.log 1.830369e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.582730e-01
## D.npnct24.log 1.582566e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 1.462750e-01
## D.npnct14.log 1.302889e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 1.257171e-01
## D.npnct08.log 1.139584e-01
## D.npnct03.log 1.084861e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 9.910833e-02
## prdline.my.fctriPad 2:.clusterid.fctr2 8.053090e-02
## prdline.my.fctriPad 2:.clusterid.fctr4 7.364192e-02
## prdline.my.fctriPad 1:.clusterid.fctr2 3.848913e-02
## prdline.my.fctriPad 2:.clusterid.fctr3 3.822434e-02
## prdline.my.fctriPad 1:.clusterid.fctr3 3.177592e-02
## prdline.my.fctriPad 2:.clusterid.fctr5 3.162311e-02
## prdline.my.fctrUnknown:.clusterid.fctr3 2.518860e-02
## D.npnct05.log 2.124534e-02
## prdline.my.fctriPad 1:.clusterid.fctr4 1.233498e-02
## prdline.my.fctriPadmini:.clusterid.fctr5 8.680101e-03
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 3+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## importance
## biddable 1.000000e+02
## prdline.my.fctriPadAir 6.017061e+01
## idseq.my 5.834247e+01
## condition.fctrNew 3.187377e+01
## prdline.my.fctriPadmini 2+ 1.584214e+01
## prdline.my.fctriPad 1 1.269167e+01
## storage.fctr16 1.111043e+01
## condition.fctrFor parts or not working 9.445925e+00
## storage.fctr64 8.479953e+00
## D.TfIdf.sum.stem.stop.Ratio 7.845998e+00
## color.fctrWhite 6.841606e+00
## D.ratio.sum.TfIdf.nwrds 6.639130e+00
## color.fctrGold 6.526682e+00
## cellular.fctr1 6.328521e+00
## D.ratio.nstopwrds.nwrds 6.086762e+00
## carrier.fctrUnknown 5.477986e+00
## color.fctrSpace Gray 5.299191e+00
## condition.fctrNew other (see details) 4.690868e+00
## prdline.my.fctriPad 3+ 4.038812e+00
## color.fctrBlack 3.712488e+00
## storage.fctr32 3.553819e+00
## carrier.fctrAT&T 3.424452e+00
## D.TfIdf.sum.post.stop 3.185491e+00
## cellular.fctrUnknown 3.045839e+00
## carrier.fctrVerizon 2.871059e+00
## storage.fctrUnknown 2.855441e+00
## D.sum.TfIdf 2.633770e+00
## D.TfIdf.sum.post.stem 2.581885e+00
## D.nuppr.log 2.449205e+00
## prdline.my.fctriPad 2 2.390203e+00
## prdline.my.fctrUnknown:.clusterid.fctr2 1.933986e+00
## prdline.my.fctriPadmini 1.785961e+00
## carrier.fctrSprint 1.755063e+00
## D.nchrs.log 1.717401e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 1.612254e+00
## D.nstopwrds.log 1.601153e+00
## D.ndgts.log 1.499496e+00
## D.npnct13.log 1.470214e+00
## D.nwrds.log 1.414854e+00
## D.npnct11.log 1.354603e+00
## carrier.fctrT-Mobile 1.040226e+00
## D.terms.n.post.stem 9.785629e-01
## D.terms.n.post.stop 9.386855e-01
## D.terms.n.post.stem.log 9.136787e-01
## D.nwrds.unq.log 8.807968e-01
## D.terms.n.post.stop.log 8.766611e-01
## D.npnct01.log 8.763425e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 7.410079e-01
## condition.fctrManufacturer refurbished 7.341349e-01
## condition.fctrSeller refurbished 7.139955e-01
## D.terms.n.stem.stop.Ratio 4.135959e-01
## carrier.fctrOther 3.500180e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 2.412510e-01
## D.npnct15.log 2.138585e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 2.006444e-01
## D.npnct16.log 1.985543e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 1.963476e-01
## prdline.my.fctriPadAir:.clusterid.fctr4 1.913993e-01
## D.npnct06.log 1.899101e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.896488e-01
## D.npnct12.log 1.830369e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.582730e-01
## D.npnct24.log 1.582566e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 1.462750e-01
## D.npnct14.log 1.302889e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 1.257171e-01
## D.npnct08.log 1.139584e-01
## D.npnct03.log 1.084861e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 9.910833e-02
## prdline.my.fctriPad 2:.clusterid.fctr2 8.053090e-02
## prdline.my.fctriPad 2:.clusterid.fctr4 7.364192e-02
## prdline.my.fctriPad 1:.clusterid.fctr2 3.848913e-02
## prdline.my.fctriPad 2:.clusterid.fctr3 3.822434e-02
## prdline.my.fctriPad 1:.clusterid.fctr3 3.177592e-02
## prdline.my.fctriPad 2:.clusterid.fctr5 3.162311e-02
## prdline.my.fctrUnknown:.clusterid.fctr3 2.518860e-02
## D.npnct05.log 2.124534e-02
## prdline.my.fctriPad 1:.clusterid.fctr4 1.233498e-02
## prdline.my.fctriPadmini:.clusterid.fctr5 8.680101e-03
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 3+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## Final.rf.importance
## biddable 1.000000e+02
## prdline.my.fctriPadAir 6.017061e+01
## idseq.my 5.834247e+01
## condition.fctrNew 3.187377e+01
## prdline.my.fctriPadmini 2+ 1.584214e+01
## prdline.my.fctriPad 1 1.269167e+01
## storage.fctr16 1.111043e+01
## condition.fctrFor parts or not working 9.445925e+00
## storage.fctr64 8.479953e+00
## D.TfIdf.sum.stem.stop.Ratio 7.845998e+00
## color.fctrWhite 6.841606e+00
## D.ratio.sum.TfIdf.nwrds 6.639130e+00
## color.fctrGold 6.526682e+00
## cellular.fctr1 6.328521e+00
## D.ratio.nstopwrds.nwrds 6.086762e+00
## carrier.fctrUnknown 5.477986e+00
## color.fctrSpace Gray 5.299191e+00
## condition.fctrNew other (see details) 4.690868e+00
## prdline.my.fctriPad 3+ 4.038812e+00
## color.fctrBlack 3.712488e+00
## storage.fctr32 3.553819e+00
## carrier.fctrAT&T 3.424452e+00
## D.TfIdf.sum.post.stop 3.185491e+00
## cellular.fctrUnknown 3.045839e+00
## carrier.fctrVerizon 2.871059e+00
## storage.fctrUnknown 2.855441e+00
## D.sum.TfIdf 2.633770e+00
## D.TfIdf.sum.post.stem 2.581885e+00
## D.nuppr.log 2.449205e+00
## prdline.my.fctriPad 2 2.390203e+00
## prdline.my.fctrUnknown:.clusterid.fctr2 1.933986e+00
## prdline.my.fctriPadmini 1.785961e+00
## carrier.fctrSprint 1.755063e+00
## D.nchrs.log 1.717401e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 1.612254e+00
## D.nstopwrds.log 1.601153e+00
## D.ndgts.log 1.499496e+00
## D.npnct13.log 1.470214e+00
## D.nwrds.log 1.414854e+00
## D.npnct11.log 1.354603e+00
## carrier.fctrT-Mobile 1.040226e+00
## D.terms.n.post.stem 9.785629e-01
## D.terms.n.post.stop 9.386855e-01
## D.terms.n.post.stem.log 9.136787e-01
## D.nwrds.unq.log 8.807968e-01
## D.terms.n.post.stop.log 8.766611e-01
## D.npnct01.log 8.763425e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 7.410079e-01
## condition.fctrManufacturer refurbished 7.341349e-01
## condition.fctrSeller refurbished 7.139955e-01
## D.terms.n.stem.stop.Ratio 4.135959e-01
## carrier.fctrOther 3.500180e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 2.412510e-01
## D.npnct15.log 2.138585e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 2.006444e-01
## D.npnct16.log 1.985543e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 1.963476e-01
## prdline.my.fctriPadAir:.clusterid.fctr4 1.913993e-01
## D.npnct06.log 1.899101e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.896488e-01
## D.npnct12.log 1.830369e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.582730e-01
## D.npnct24.log 1.582566e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 1.462750e-01
## D.npnct14.log 1.302889e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 1.257171e-01
## D.npnct08.log 1.139584e-01
## D.npnct03.log 1.084861e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 9.910833e-02
## prdline.my.fctriPad 2:.clusterid.fctr2 8.053090e-02
## prdline.my.fctriPad 2:.clusterid.fctr4 7.364192e-02
## prdline.my.fctriPad 1:.clusterid.fctr2 3.848913e-02
## prdline.my.fctriPad 2:.clusterid.fctr3 3.822434e-02
## prdline.my.fctriPad 1:.clusterid.fctr3 3.177592e-02
## prdline.my.fctriPad 2:.clusterid.fctr5 3.162311e-02
## prdline.my.fctrUnknown:.clusterid.fctr3 2.518860e-02
## D.npnct05.log 2.124534e-02
## prdline.my.fctriPad 1:.clusterid.fctr4 1.233498e-02
## prdline.my.fctriPadmini:.clusterid.fctr5 8.680101e-03
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 3+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 38
## UniqueID description biddable startprice condition cellular carrier
## 1358 11359 0 595.00 New Unknown Unknown
## 1299 11300 1 650.00 New 1 Unknown
## 1704 11705 0 590.00 New Unknown Unknown
## 1447 11448 0 4.69 New Unknown Unknown
## 792 10792 1 550.00 Used 0 None
## color storage productline .src .grpid .rnorm idseq.my
## 1358 Unknown Unknown Unknown Train <NA> -0.3914823 1359
## 1299 Gold 128 iPad Air 2 Train <NA> 1.3370014 1300
## 1704 White 16 Unknown Train <NA> -0.9839429 1705
## 1447 Unknown Unknown Unknown Train <NA> -0.7916194 1448
## 792 Gold 128 iPad Air 2 Train <NA> -0.2098595 792
## prdline.my startprice.log descr.my condition.fctr cellular.fctr
## 1358 Unknown 6.388561 New Unknown
## 1299 iPadAir 6.476972 New 1
## 1704 Unknown 6.380123 New Unknown
## 1447 Unknown 1.545433 New Unknown
## 792 iPadAir 6.309918 Used 0
## carrier.fctr color.fctr storage.fctr prdline.my.fctr
## 1358 Unknown Unknown Unknown Unknown
## 1299 Unknown Gold 128 iPadAir
## 1704 Unknown White 16 Unknown
## 1447 Unknown Unknown Unknown Unknown
## 792 None Gold 128 iPadAir
## D.terms.n.post.stop D.terms.n.post.stop.log D.TfIdf.sum.post.stop
## 1358 0 0 0
## 1299 0 0 0
## 1704 0 0 0
## 1447 0 0 0
## 792 0 0 0
## D.terms.n.post.stem D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 1358 0 0 0
## 1299 0 0 0
## 1704 0 0 0
## 1447 0 0 0
## 792 0 0 0
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.condit
## 1358 1 1 0
## 1299 1 1 0
## 1704 1 1 0
## 1447 1 1 0
## 792 1 1 0
## D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen D.T.great
## 1358 0 0 0 0 0 0 0
## 1299 0 0 0 0 0 0 0
## 1704 0 0 0 0 0 0 0
## 1447 0 0 0 0 0 0 0
## 792 0 0 0 0 0 0 0
## D.T.work D.T.excel D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 1358 0 0 0 0 0
## 1299 0 0 0 0 0
## 1704 0 0 0 0 0
## 1447 0 0 0 0 0
## 792 0 0 0 0 0
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log D.ndgts.log
## 1358 0 0 0 0
## 1299 0 0 0 0
## 1704 0 0 0 0
## 1447 0 0 0 0
## 792 0 0 0 0
## D.npnct01.log D.npnct03.log D.npnct05.log D.npnct06.log D.npnct08.log
## 1358 0 0 0 0 0
## 1299 0 0 0 0 0
## 1704 0 0 0 0 0
## 1447 0 0 0 0 0
## 792 0 0 0 0 0
## D.npnct11.log D.npnct12.log D.npnct13.log D.npnct14.log D.npnct15.log
## 1358 0 0 0 0 0
## 1299 0 0 0 0 0
## 1704 0 0 0 0 0
## 1447 0 0 0 0 0
## 792 0 0 0 0 0
## D.npnct16.log D.npnct24.log D.nstopwrds.log D.ratio.nstopwrds.nwrds
## 1358 0 0 0 1
## 1299 0 0 0 1
## 1704 0 0 0 1
## 1447 0 0 0 1
## 792 0 0 0 1
## D.P.mini D.P.air .clusterid .clusterid.fctr
## 1358 0 0 1 1
## 1299 0 0 1 1
## 1704 0 0 1 1
## 1447 0 0 1 1
## 792 0 0 1 1
## startprice.predict.Final.rf startprice.predict.Final.rf.err .label
## 1358 333.5124 261.4876 11359
## 1299 397.7206 252.2794 11300
## 1704 401.2191 188.7809 11705
## 1447 182.8442 178.1542 11448
## 792 377.7852 172.2148 10792
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "startprice.predict.Final.rf" "startprice.predict.Final.rf.err"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 fit.data.training 8 1 292.947 298.56 5.613
## 16 predict.data.new 9 0 298.560 NA NAStep 9.0: predict data new
# Compute final model predictions
# sav_newobs_df <- glb_newobs_df
# startprice.pred stuff
tmp_allobs_df <- glb_get_predictions(glb_allobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## UniqueID
## 2623 12625
## 1396 11397
## 1418 11419
## 2501 12503
## 2507 12509
## 1487 11488
## description
## 2623 Lot of 10 mixed iPad minis. Colors,models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 2507
## 1487
## biddable startprice condition cellular carrier
## 2623 0 999.99 For parts or not working Unknown Unknown
## 1396 0 999.00 Used 0 None
## 1418 1 700.00 Used Unknown Unknown
## 2501 1 879.99 New 0 None
## 2507 1 729.99 New 0 None
## 1487 1 729.99 New 0 None
## color storage productline .src .grpid .rnorm idseq.my
## 2623 White Unknown Unknown Test <NA> -0.9259777 2625
## 1396 Unknown 32 iPad mini Test <NA> -0.1429904 1397
## 1418 Unknown Unknown Unknown Test <NA> 0.7258252 1419
## 2501 Space Gray 128 iPad Air 2 Test <NA> 1.7466852 2503
## 2507 Gold 128 iPad mini 3 Test <NA> 0.8371948 2509
## 1487 Space Gray 128 iPad mini 3 Test <NA> 0.7174519 1488
## prdline.my startprice.log
## 2623 iPadmini 6.907745
## 1396 iPadmini 6.906755
## 1418 Unknown 6.551080
## 2501 iPadAir 6.779911
## 2507 iPadmini 2+ 6.593031
## 1487 iPadmini 2+ 6.593031
## descr.my
## 2623 Lot of 10 mixed iPad minis. Colors, models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 2507
## 1487
## condition.fctr cellular.fctr carrier.fctr color.fctr
## 2623 For parts or not working Unknown Unknown White
## 1396 Used 0 None Unknown
## 1418 Used Unknown Unknown Unknown
## 2501 New 0 None Space Gray
## 2507 New 0 None Gold
## 1487 New 0 None Space Gray
## storage.fctr prdline.my.fctr D.terms.n.post.stop
## 2623 Unknown iPadmini 8
## 1396 32 iPadmini 0
## 1418 Unknown Unknown 0
## 2501 128 iPadAir 0
## 2507 128 iPadmini 2+ 0
## 1487 128 iPadmini 2+ 0
## D.terms.n.post.stop.log D.TfIdf.sum.post.stop D.terms.n.post.stem
## 2623 2.197225 9.127623 8
## 1396 0.000000 0.000000 0
## 1418 0.000000 0.000000 0
## 2501 0.000000 0.000000 0
## 2507 0.000000 0.000000 0
## 1487 0.000000 0.000000 0
## D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 2623 2.197225 8.069403
## 1396 0.000000 0.000000
## 1418 0.000000 0.000000
## 2501 0.000000 0.000000
## 2507 0.000000 0.000000
## 1487 0.000000 0.000000
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.condit
## 2623 1 0.884064 0
## 1396 1 1.000000 0
## 1418 1 1.000000 0
## 2501 1 1.000000 0
## 2507 1 1.000000 0
## 1487 1 1.000000 0
## D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen D.T.great
## 2623 0 0 0 0 0.3908446 0 0
## 1396 0 0 0 0 0.0000000 0 0
## 1418 0 0 0 0 0.0000000 0 0
## 2501 0 0 0 0 0.0000000 0 0
## 2507 0 0 0 0 0.0000000 0 0
## 1487 0 0 0 0 0.0000000 0 0
## D.T.work D.T.excel D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 2623 0 0 2.944439 2.197225 8.069403
## 1396 0 0 0.000000 0.000000 0.000000
## 1418 0 0 0.000000 0.000000 0.000000
## 2501 0 0 0.000000 0.000000 0.000000
## 2507 0 0 0.000000 0.000000 0.000000
## 1487 0 0 0.000000 0.000000 0.000000
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log D.ndgts.log
## 2623 0.4483002 4.634729 4.356709 1.098612
## 1396 0.0000000 0.000000 0.000000 0.000000
## 1418 0.0000000 0.000000 0.000000 0.000000
## 2501 0.0000000 0.000000 0.000000 0.000000
## 2507 0.0000000 0.000000 0.000000 0.000000
## 1487 0.0000000 0.000000 0.000000 0.000000
## D.npnct01.log D.npnct03.log D.npnct05.log D.npnct06.log D.npnct08.log
## 2623 0 0 0 0.6931472 0
## 1396 0 0 0 0.0000000 0
## 1418 0 0 0 0.0000000 0
## 2501 0 0 0 0.0000000 0
## 2507 0 0 0 0.0000000 0
## 1487 0 0 0 0.0000000 0
## D.npnct11.log D.npnct12.log D.npnct13.log D.npnct14.log D.npnct15.log
## 2623 0.6931472 0 1.098612 0 0
## 1396 0.0000000 0 0.000000 0 0
## 1418 0.0000000 0 0.000000 0 0
## 2501 0.0000000 0 0.000000 0 0
## 2507 0.0000000 0 0.000000 0 0
## 1487 0.0000000 0 0.000000 0 0
## D.npnct16.log D.npnct24.log D.nstopwrds.log D.ratio.nstopwrds.nwrds
## 2623 0.6931472 0.6931472 2.197225 0.4736842
## 1396 0.0000000 0.0000000 0.000000 1.0000000
## 1418 0.0000000 0.0000000 0.000000 1.0000000
## 2501 0.0000000 0.0000000 0.000000 1.0000000
## 2507 0.0000000 0.0000000 0.000000 1.0000000
## 1487 0.0000000 0.0000000 0.000000 1.0000000
## D.P.mini D.P.air .clusterid .clusterid.fctr .lcn
## 2623 1 0 4 4 OOB
## 1396 0 0 1 1 OOB
## 1418 0 0 1 1 OOB
## 2501 0 0 1 1 OOB
## 2507 0 0 1 1 OOB
## 1487 0 0 1 1 OOB
## startprice.predict.All.X.no.rnorm.rf
## 2623 152.91255
## 1396 208.42139
## 1418 77.88643
## 2501 258.00443
## 2507 122.67522
## 1487 133.23766
## startprice.predict.All.X.no.rnorm.rf.err
## 2623 847.0774
## 1396 790.5786
## 1418 622.1136
## 2501 621.9856
## 2507 607.3148
## 1487 596.7523
## startprice.predict.All.X.no.rnorm.rf.accurate
## 2623 FALSE
## 1396 FALSE
## 1418 FALSE
## 2501 FALSE
## 2507 FALSE
## 1487 FALSE
## startprice.predict.Final.rf startprice.predict.Final.rf.err
## 2623 152.91255 847.0774
## 1396 208.42139 790.5786
## 1418 77.88643 622.1136
## 2501 258.00443 621.9856
## 2507 122.67522 607.3148
## 1487 133.23766 596.7523rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
tmp_allobs_df <- tmp_allobs_df[, c(glb_id_var, glb_rsp_var, rsp_var_out)]
names(tmp_allobs_df)[3] <- glb_rsp_var_out
write.csv(tmp_allobs_df, paste0(glb_out_pfx, "predict.csv"), row.names=FALSE)
##
glb_newobs_df <- glb_get_predictions(glb_newobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## UniqueID
## 2623 12625
## 1396 11397
## 1418 11419
## 2501 12503
## 2507 12509
## 1487 11488
## description
## 2623 Lot of 10 mixed iPad minis. Colors,models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 2507
## 1487
## biddable startprice condition cellular carrier
## 2623 0 999.99 For parts or not working Unknown Unknown
## 1396 0 999.00 Used 0 None
## 1418 1 700.00 Used Unknown Unknown
## 2501 1 879.99 New 0 None
## 2507 1 729.99 New 0 None
## 1487 1 729.99 New 0 None
## color storage productline .src .grpid .rnorm idseq.my
## 2623 White Unknown Unknown Test <NA> -0.9259777 2625
## 1396 Unknown 32 iPad mini Test <NA> -0.1429904 1397
## 1418 Unknown Unknown Unknown Test <NA> 0.7258252 1419
## 2501 Space Gray 128 iPad Air 2 Test <NA> 1.7466852 2503
## 2507 Gold 128 iPad mini 3 Test <NA> 0.8371948 2509
## 1487 Space Gray 128 iPad mini 3 Test <NA> 0.7174519 1488
## prdline.my startprice.log
## 2623 iPadmini 6.907745
## 1396 iPadmini 6.906755
## 1418 Unknown 6.551080
## 2501 iPadAir 6.779911
## 2507 iPadmini 2+ 6.593031
## 1487 iPadmini 2+ 6.593031
## descr.my
## 2623 Lot of 10 mixed iPad minis. Colors, models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 2507
## 1487
## condition.fctr cellular.fctr carrier.fctr color.fctr
## 2623 For parts or not working Unknown Unknown White
## 1396 Used 0 None Unknown
## 1418 Used Unknown Unknown Unknown
## 2501 New 0 None Space Gray
## 2507 New 0 None Gold
## 1487 New 0 None Space Gray
## storage.fctr prdline.my.fctr D.terms.n.post.stop
## 2623 Unknown iPadmini 8
## 1396 32 iPadmini 0
## 1418 Unknown Unknown 0
## 2501 128 iPadAir 0
## 2507 128 iPadmini 2+ 0
## 1487 128 iPadmini 2+ 0
## D.terms.n.post.stop.log D.TfIdf.sum.post.stop D.terms.n.post.stem
## 2623 2.197225 9.127623 8
## 1396 0.000000 0.000000 0
## 1418 0.000000 0.000000 0
## 2501 0.000000 0.000000 0
## 2507 0.000000 0.000000 0
## 1487 0.000000 0.000000 0
## D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 2623 2.197225 8.069403
## 1396 0.000000 0.000000
## 1418 0.000000 0.000000
## 2501 0.000000 0.000000
## 2507 0.000000 0.000000
## 1487 0.000000 0.000000
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.condit
## 2623 1 0.884064 0
## 1396 1 1.000000 0
## 1418 1 1.000000 0
## 2501 1 1.000000 0
## 2507 1 1.000000 0
## 1487 1 1.000000 0
## D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen D.T.great
## 2623 0 0 0 0 0.3908446 0 0
## 1396 0 0 0 0 0.0000000 0 0
## 1418 0 0 0 0 0.0000000 0 0
## 2501 0 0 0 0 0.0000000 0 0
## 2507 0 0 0 0 0.0000000 0 0
## 1487 0 0 0 0 0.0000000 0 0
## D.T.work D.T.excel D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 2623 0 0 2.944439 2.197225 8.069403
## 1396 0 0 0.000000 0.000000 0.000000
## 1418 0 0 0.000000 0.000000 0.000000
## 2501 0 0 0.000000 0.000000 0.000000
## 2507 0 0 0.000000 0.000000 0.000000
## 1487 0 0 0.000000 0.000000 0.000000
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log D.ndgts.log
## 2623 0.4483002 4.634729 4.356709 1.098612
## 1396 0.0000000 0.000000 0.000000 0.000000
## 1418 0.0000000 0.000000 0.000000 0.000000
## 2501 0.0000000 0.000000 0.000000 0.000000
## 2507 0.0000000 0.000000 0.000000 0.000000
## 1487 0.0000000 0.000000 0.000000 0.000000
## D.npnct01.log D.npnct03.log D.npnct05.log D.npnct06.log D.npnct08.log
## 2623 0 0 0 0.6931472 0
## 1396 0 0 0 0.0000000 0
## 1418 0 0 0 0.0000000 0
## 2501 0 0 0 0.0000000 0
## 2507 0 0 0 0.0000000 0
## 1487 0 0 0 0.0000000 0
## D.npnct11.log D.npnct12.log D.npnct13.log D.npnct14.log D.npnct15.log
## 2623 0.6931472 0 1.098612 0 0
## 1396 0.0000000 0 0.000000 0 0
## 1418 0.0000000 0 0.000000 0 0
## 2501 0.0000000 0 0.000000 0 0
## 2507 0.0000000 0 0.000000 0 0
## 1487 0.0000000 0 0.000000 0 0
## D.npnct16.log D.npnct24.log D.nstopwrds.log D.ratio.nstopwrds.nwrds
## 2623 0.6931472 0.6931472 2.197225 0.4736842
## 1396 0.0000000 0.0000000 0.000000 1.0000000
## 1418 0.0000000 0.0000000 0.000000 1.0000000
## 2501 0.0000000 0.0000000 0.000000 1.0000000
## 2507 0.0000000 0.0000000 0.000000 1.0000000
## 1487 0.0000000 0.0000000 0.000000 1.0000000
## D.P.mini D.P.air .clusterid .clusterid.fctr
## 2623 1 0 4 4
## 1396 0 0 1 1
## 1418 0 0 1 1
## 2501 0 0 1 1
## 2507 0 0 1 1
## 1487 0 0 1 1
## startprice.predict.Final.rf startprice.predict.Final.rf.err
## 2623 152.91255 847.0774
## 1396 208.42139 790.5786
## 1418 77.88643 622.1136
## 2501 258.00443 621.9856
## 2507 122.67522 607.3148
## 1487 133.23766 596.7523if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 38
## UniqueID
## 2623 12625
## 1396 11397
## 1418 11419
## 2501 12503
## 2507 12509
## description
## 2623 Lot of 10 mixed iPad minis. Colors,models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 2507
## biddable startprice condition cellular carrier
## 2623 0 999.99 For parts or not working Unknown Unknown
## 1396 0 999.00 Used 0 None
## 1418 1 700.00 Used Unknown Unknown
## 2501 1 879.99 New 0 None
## 2507 1 729.99 New 0 None
## color storage productline .src .grpid .rnorm idseq.my
## 2623 White Unknown Unknown Test <NA> -0.9259777 2625
## 1396 Unknown 32 iPad mini Test <NA> -0.1429904 1397
## 1418 Unknown Unknown Unknown Test <NA> 0.7258252 1419
## 2501 Space Gray 128 iPad Air 2 Test <NA> 1.7466852 2503
## 2507 Gold 128 iPad mini 3 Test <NA> 0.8371948 2509
## prdline.my startprice.log
## 2623 iPadmini 6.907745
## 1396 iPadmini 6.906755
## 1418 Unknown 6.551080
## 2501 iPadAir 6.779911
## 2507 iPadmini 2+ 6.593031
## descr.my
## 2623 Lot of 10 mixed iPad minis. Colors, models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 2507
## condition.fctr cellular.fctr carrier.fctr color.fctr
## 2623 For parts or not working Unknown Unknown White
## 1396 Used 0 None Unknown
## 1418 Used Unknown Unknown Unknown
## 2501 New 0 None Space Gray
## 2507 New 0 None Gold
## storage.fctr prdline.my.fctr D.terms.n.post.stop
## 2623 Unknown iPadmini 8
## 1396 32 iPadmini 0
## 1418 Unknown Unknown 0
## 2501 128 iPadAir 0
## 2507 128 iPadmini 2+ 0
## D.terms.n.post.stop.log D.TfIdf.sum.post.stop D.terms.n.post.stem
## 2623 2.197225 9.127623 8
## 1396 0.000000 0.000000 0
## 1418 0.000000 0.000000 0
## 2501 0.000000 0.000000 0
## 2507 0.000000 0.000000 0
## D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 2623 2.197225 8.069403
## 1396 0.000000 0.000000
## 1418 0.000000 0.000000
## 2501 0.000000 0.000000
## 2507 0.000000 0.000000
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.condit
## 2623 1 0.884064 0
## 1396 1 1.000000 0
## 1418 1 1.000000 0
## 2501 1 1.000000 0
## 2507 1 1.000000 0
## D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen D.T.great
## 2623 0 0 0 0 0.3908446 0 0
## 1396 0 0 0 0 0.0000000 0 0
## 1418 0 0 0 0 0.0000000 0 0
## 2501 0 0 0 0 0.0000000 0 0
## 2507 0 0 0 0 0.0000000 0 0
## D.T.work D.T.excel D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 2623 0 0 2.944439 2.197225 8.069403
## 1396 0 0 0.000000 0.000000 0.000000
## 1418 0 0 0.000000 0.000000 0.000000
## 2501 0 0 0.000000 0.000000 0.000000
## 2507 0 0 0.000000 0.000000 0.000000
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log D.ndgts.log
## 2623 0.4483002 4.634729 4.356709 1.098612
## 1396 0.0000000 0.000000 0.000000 0.000000
## 1418 0.0000000 0.000000 0.000000 0.000000
## 2501 0.0000000 0.000000 0.000000 0.000000
## 2507 0.0000000 0.000000 0.000000 0.000000
## D.npnct01.log D.npnct03.log D.npnct05.log D.npnct06.log D.npnct08.log
## 2623 0 0 0 0.6931472 0
## 1396 0 0 0 0.0000000 0
## 1418 0 0 0 0.0000000 0
## 2501 0 0 0 0.0000000 0
## 2507 0 0 0 0.0000000 0
## D.npnct11.log D.npnct12.log D.npnct13.log D.npnct14.log D.npnct15.log
## 2623 0.6931472 0 1.098612 0 0
## 1396 0.0000000 0 0.000000 0 0
## 1418 0.0000000 0 0.000000 0 0
## 2501 0.0000000 0 0.000000 0 0
## 2507 0.0000000 0 0.000000 0 0
## D.npnct16.log D.npnct24.log D.nstopwrds.log D.ratio.nstopwrds.nwrds
## 2623 0.6931472 0.6931472 2.197225 0.4736842
## 1396 0.0000000 0.0000000 0.000000 1.0000000
## 1418 0.0000000 0.0000000 0.000000 1.0000000
## 2501 0.0000000 0.0000000 0.000000 1.0000000
## 2507 0.0000000 0.0000000 0.000000 1.0000000
## D.P.mini D.P.air .clusterid .clusterid.fctr
## 2623 1 0 4 4
## 1396 0 0 1 1
## 1418 0 0 1 1
## 2501 0 0 1 1
## 2507 0 0 1 1
## startprice.predict.Final.rf startprice.predict.Final.rf.err .label
## 2623 152.91255 847.0774 12625
## 1396 208.42139 790.5786 11397
## 1418 77.88643 622.1136 11419
## 2501 258.00443 621.9856 12503
## 2507 122.67522 607.3148 12509
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
# submit_df <- glb_newobs_df[, c(paste0(glb_rsp_var_out, glb_fin_mdl_id)), FALSE]
# names(submit_df)[1] <- "BDscience"
# submit_df$BDscience <- as.numeric(submit_df$BDscience) - 1
# #submit_df <-rbind(submit_df, data.frame(bdanalytics=c(" ")))
# print("Submission Stats:")
# print(table(submit_df$BDscience, useNA = "ifany"))
glb_force_prediction_lst <- list()
glb_force_prediction_lst[["0"]] <- c(11885, 11907, 11943,
12115, 12253, 12285, 12367, 12388, 12585)
for (obs_id in glb_force_prediction_lst[["0"]])
submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
max(0, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] - 0.5)
glb_force_prediction_lst[["1"]] <- c(11871, 11875, 11886,
11913, 11931, 11937, 11967, 11990, 11994, 11999,
12000, 12002, 12021, 12065, 12072,
12111, 12114, 12126, 12152, 12172,
12213, 12214, 12233, 12278, 12299,
12446, 12491,
12505, 12576, 12608, 12630)
for (obs_id in glb_force_prediction_lst[["1"]])
submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
min(0.9999, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] + 0.5)
} else submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id))]
if (glb_is_classification) {
rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
tmp_newobs_df <- subset(glb_newobs_df[, c(glb_id_var, ".grpid", rsp_var_out)],
!is.na(.grpid))
tmp_newobs_df <- merge(tmp_newobs_df, dupgrps_df, by=".grpid", all.x=TRUE)
tmp_newobs_df <- merge(tmp_newobs_df, submit_df, by=glb_id_var, all.x = TRUE)
tmp_newobs_df$.err <-
((tmp_newobs_df$Probability1 >= 0.5) & (tmp_newobs_df$sold.0 > 0) |
(tmp_newobs_df$Probability1 <= 0.5) & (tmp_newobs_df$sold.1 > 0))
tmp_newobs_df <- orderBy(~UniqueID, subset(tmp_newobs_df, .err == TRUE))
print("Prediction errors in duplicates:")
print(tmp_newobs_df)
if (nrow(tmp_newobs_df) > 0)
stop("check Prediction errors in duplicates")
#print(dupobs_df[dupobs_df$.grpid == 26, ])
if (max(glb_newobs_df[!is.na(glb_newobs_df[, rsp_var_out]) &
(glb_newobs_df[, rsp_var_out] == "Y"), "startprice"]) >
max(glb_allobs_df[!is.na(glb_allobs_df[, glb_rsp_var]) &
(glb_allobs_df[, glb_rsp_var] == "Y"), "startprice"]))
stop("startprice for some +ve predictions > 675")
}
submit_fname <- paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv")
write.csv(submit_df, submit_fname, quote=FALSE, row.names=FALSE)
#cat(" ", "\n", file=submit_fn, append=TRUE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
for (txt_var in glb_txt_vars) {
# Print post-stem-words but need post-stop-words for debugging ?
print(sprintf(" All post-stem-words TfIDf terms for %s:", txt_var))
myprint_df(glb_post_stem_words_terms_df_lst[[txt_var]])
TfIdf_mtrx <- glb_post_stem_words_TfIdf_mtrx_lst[[txt_var]]
print(glb_allobs_df[
which(TfIdf_mtrx[, tail(glb_post_stem_words_terms_df_lst[[txt_var]], 1)$pos] > 0),
c(glb_id_var, glb_txt_vars)])
print(nrow(subset(glb_post_stem_words_terms_df_lst[[txt_var]], freq == 1)))
#print(glb_allobs_df[which(TfIdf_mtrx[, 207] > 0), c(glb_id_var, glb_txt_vars)])
#unlist(strsplit(glb_allobs_df[2157, "description"], ""))
#glb_allobs_df[2442, c(glb_id_var, glb_txt_vars)]
#TfIdf_mtrx[2442, TfIdf_mtrx[2442, ] > 0]
print(sprintf(" Top_n post_stem_words TfIDf terms for %s:", txt_var))
tmp_df <- glb_post_stem_words_terms_df_lst[[txt_var]]
top_n_vctr <- tmp_df$term[1:glb_top_n[[txt_var]]]
tmp_freq1_df <- subset(tmp_df, freq == 1)
tmp_freq1_df$top_n <- grepl(paste0(top_n_vctr, collapse="|"), tmp_freq1_df$term)
print(subset(tmp_freq1_df, top_n == TRUE))
}## [1] " All post-stem-words TfIDf terms for descr.my:"
## TfIdf term freq pos
## condit 208.1066 condit 496 122
## use 146.5910 use 291 559
## scratch 128.3886 scratch 286 457
## new 125.5866 new 156 346
## good 121.0564 good 197 233
## ipad 107.4871 ipad 232 275
## TfIdf term freq pos
## excel 98.970629 excel 100 187
## item 68.803935 item 129 278
## set 17.367419 set 14 469
## unabl 4.192969 unabl 4 545
## passcod 3.535502 passcod 3 380
## condition 2.449790 condition 2 123
## TfIdf term freq pos
## remot 1.2639536 remot 1 437
## ringer 1.2639536 ringer 1 450
## septemb 1.2639536 septemb 1 468
## site 1.2639536 site 1 487
## 975 1.1375583 975 1 16
## 79in 0.9479652 79in 1 15
## UniqueID
## 520 10520
## descr.my
## 520 Apple iPad mini 1st Generation 16GB, Wi- Fi, 7.9in - Space Gray, great condition comes with the
## [1] 123
## [1] " Top_n post_stem_words TfIDf terms for descr.my:"
## [1] TfIdf term freq pos top_n
## <0 rows> (or 0-length row.names)if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])
print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: All.X.no.rnorm.rf"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.rf"print(dim(glb_fitobs_df))## [1] 860 66print(dsp_models_df)## model_id min.RMSE.OOB max.R.sq.OOB
## 13 All.X.no.rnorm.rf 130.2892 0.6249481535
## 7 Low.cor.X.lm 131.2443 0.6201210933
## 25 csm.rf 131.3387 0.6189451942
## 17 All.Interact.X.glmnet 133.0815 0.6094109698
## 19 All.Interact.X.no.rnorm.rf 133.3578 0.6070976254
## 16 All.Interact.X.bayesglm 133.6435 0.6061055541
## 10 All.X.bayesglm 134.0346 0.6037971836
## 14 All.Interact.X.lm 134.2138 0.6027366015
## 15 All.Interact.X.glm 134.2138 0.6027366015
## 23 csm.glmnet 134.3057 0.6021921418
## 9 All.X.glm 134.7805 0.5993746699
## 8 All.X.lm 134.7805 0.5993746699
## 22 csm.bayesglm 134.8527 0.5989458091
## 21 csm.glm 135.1112 0.5974058441
## 20 csm.lm 135.1112 0.5974058441
## 11 All.X.glmnet 138.1274 0.5792308893
## 3 Max.cor.Y.cv.0.cp.0.rpart 143.0090 0.5489639195
## 6 Interact.High.cor.Y.lm 147.3050 0.5214588759
## 5 Max.cor.Y.lm 147.7389 0.5186350614
## 12 All.X.no.rnorm.rpart 157.8425 0.4505450166
## 24 csm.rpart 157.8425 0.4505450166
## 4 Max.cor.Y.rpart 157.8425 0.4505450166
## 18 All.Interact.X.no.rnorm.rpart 176.1188 0.3159370407
## 1 MFO.lm 212.9262 0.0001316983
## 2 Max.cor.Y.cv.0.rpart 212.9402 0.0000000000
## max.Adj.R.sq.fit
## 13 NA
## 7 0.575613356
## 25 NA
## 17 NA
## 19 NA
## 16 NA
## 10 NA
## 14 0.605933384
## 15 NA
## 23 NA
## 9 NA
## 8 0.578508269
## 22 NA
## 21 NA
## 20 0.556393828
## 11 NA
## 3 NA
## 6 0.463501759
## 5 0.454975555
## 12 NA
## 24 NA
## 4 NA
## 18 NA
## 1 -0.001093153
## 2 NAif (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_var)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_var, glb_rsp_var,
predct_error_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "error.abs.OOB"
sOOB_ctgry_df <- dplyr::group_by(tmp_OOBobs_df, prdline.my)
sOOB_ctgry_df <- dplyr::count(sOOB_ctgry_df,
startprice.OOB.sum = sum(startprice),
err.abs.OOB.sum = sum(error.abs.OOB),
err.abs.OOB.mean = mean(error.abs.OOB))
names(sOOB_ctgry_df)[4] <- ".n.OOB"
sOOB_ctgry_df <- dplyr::ungroup(sOOB_ctgry_df)
#intersect(names(glb_ctgry_df), names(sOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, sOOB_ctgry_df, all=TRUE)
print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
model_id_method=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
}## [1] "All.X.no.rnorm.rf OOB RMSE: 130.2892"
## .n.OOB prdline.my .n.Tst .freqRatio.Tst .freqRatio.OOB
## 7 340 iPadAir 340 0.1892042 0.1892042
## 2 205 Unknown 205 0.1140790 0.1140790
## 3 219 iPadmini 2+ 219 0.1218698 0.1218698
## 5 289 iPad 3+ 289 0.1608236 0.1608236
## 4 260 iPadmini 260 0.1446856 0.1446856
## 6 295 iPad 2 295 0.1641625 0.1641625
## 1 189 iPad 1 189 0.1051753 0.1051753
## startprice.OOB.sum err.abs.OOB.sum err.abs.OOB.mean
## 7 143765.13 43034.728 126.57273
## 2 41689.51 23291.970 113.61937
## 3 73254.02 24313.862 111.02220
## 5 73469.35 25379.872 87.81963
## 4 50772.66 18088.001 69.56924
## 6 47365.96 16813.279 56.99416
## 1 19462.87 7212.003 38.15875
## [1] "Final.rf prediction stats for glb_newobs_df:"
## model_id max.R.sq.new min.RMSE.new
## 1 Final.rf 0.6249482 130.2892if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_var)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_var, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
print(glb_OOBobs_df[(glb_OOBobs_df$prdline.my == "iPadAir") &
!(glb_OOBobs_df[, predct_accurate_var_name]),
c(glb_id_var, glb_rsp_var_raw,
#"description"
"biddable", "startprice", "condition"
)])
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
}
dsp_myCategory_conf_mtrx <- function(myCategory) {
print(sprintf("%s OOB::myCategory=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, myCategory))
print(t(confusionMatrix(
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, glb_rsp_var])$table))
print(sum(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
predct_accurate_var_name]) /
nrow(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, ]))
err_ids <- glb_OOBobs_df[(glb_OOBobs_df$myCategory == myCategory) &
(!glb_OOBobs_df[, predct_accurate_var_name]), glb_id_var]
OOB_FNerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 1),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FN errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FNerr_df)))
print(OOB_FNerr_df)
OOB_FPerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 0),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FP errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FPerr_df)))
print(OOB_FPerr_df)
}
#dsp_myCategory_conf_mtrx(myCategory="OpEd#Opinion#")
#dsp_myCategory_conf_mtrx(myCategory="Business#Business Day#Dealbook")
#dsp_myCategory_conf_mtrx(myCategory="##")
# if (glb_is_classification) {
# print("FN_OOB_ids:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# print(dsp_vctr <- colSums(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# setdiff(grep("[HSA].", names(glb_OOBobs_df), value=TRUE),
# union(myfind_chr_cols_df(glb_OOBobs_df),
# grep(".fctr", names(glb_OOBobs_df), fixed=TRUE, value=TRUE)))]))
# }
dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBobs_df[glb_OOBobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newobs_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_newobs_df),
grep(".fctr", names(glb_newobs_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newobs_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), glb_featsimp_df))## All.X.no.rnorm.rf.importance
## biddable 1.000000e+02
## prdline.my.fctriPadAir 6.017061e+01
## idseq.my 5.834247e+01
## condition.fctrNew 3.187377e+01
## prdline.my.fctriPadmini 2+ 1.584214e+01
## prdline.my.fctriPad 1 1.269167e+01
## storage.fctr16 1.111043e+01
## condition.fctrFor parts or not working 9.445925e+00
## storage.fctr64 8.479953e+00
## D.TfIdf.sum.stem.stop.Ratio 7.845998e+00
## color.fctrWhite 6.841606e+00
## D.ratio.sum.TfIdf.nwrds 6.639130e+00
## color.fctrGold 6.526682e+00
## cellular.fctr1 6.328521e+00
## D.ratio.nstopwrds.nwrds 6.086762e+00
## carrier.fctrUnknown 5.477986e+00
## color.fctrSpace Gray 5.299191e+00
## condition.fctrNew other (see details) 4.690868e+00
## prdline.my.fctriPad 3+ 4.038812e+00
## color.fctrBlack 3.712488e+00
## storage.fctr32 3.553819e+00
## carrier.fctrAT&T 3.424452e+00
## D.TfIdf.sum.post.stop 3.185491e+00
## cellular.fctrUnknown 3.045839e+00
## carrier.fctrVerizon 2.871059e+00
## storage.fctrUnknown 2.855441e+00
## D.sum.TfIdf 2.633770e+00
## D.TfIdf.sum.post.stem 2.581885e+00
## D.nuppr.log 2.449205e+00
## prdline.my.fctriPad 2 2.390203e+00
## prdline.my.fctrUnknown:.clusterid.fctr2 1.933986e+00
## prdline.my.fctriPadmini 1.785961e+00
## carrier.fctrSprint 1.755063e+00
## D.nchrs.log 1.717401e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 1.612254e+00
## D.nstopwrds.log 1.601153e+00
## D.ndgts.log 1.499496e+00
## D.npnct13.log 1.470214e+00
## D.nwrds.log 1.414854e+00
## D.npnct11.log 1.354603e+00
## carrier.fctrT-Mobile 1.040226e+00
## D.terms.n.post.stem 9.785629e-01
## D.terms.n.post.stop 9.386855e-01
## D.terms.n.post.stem.log 9.136787e-01
## D.nwrds.unq.log 8.807968e-01
## D.terms.n.post.stop.log 8.766611e-01
## D.npnct01.log 8.763425e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 7.410079e-01
## condition.fctrManufacturer refurbished 7.341349e-01
## condition.fctrSeller refurbished 7.139955e-01
## D.terms.n.stem.stop.Ratio 4.135959e-01
## carrier.fctrOther 3.500180e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 2.412510e-01
## D.npnct15.log 2.138585e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 2.006444e-01
## D.npnct16.log 1.985543e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 1.963476e-01
## prdline.my.fctriPadAir:.clusterid.fctr4 1.913993e-01
## D.npnct06.log 1.899101e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.896488e-01
## D.npnct12.log 1.830369e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.582730e-01
## D.npnct24.log 1.582566e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 1.462750e-01
## D.npnct14.log 1.302889e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 1.257171e-01
## D.npnct08.log 1.139584e-01
## D.npnct03.log 1.084861e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 9.910833e-02
## prdline.my.fctriPad 2:.clusterid.fctr2 8.053090e-02
## prdline.my.fctriPad 2:.clusterid.fctr4 7.364192e-02
## prdline.my.fctriPad 1:.clusterid.fctr2 3.848913e-02
## prdline.my.fctriPad 2:.clusterid.fctr3 3.822434e-02
## prdline.my.fctriPad 1:.clusterid.fctr3 3.177592e-02
## prdline.my.fctriPad 2:.clusterid.fctr5 3.162311e-02
## prdline.my.fctrUnknown:.clusterid.fctr3 2.518860e-02
## D.npnct05.log 2.124534e-02
## prdline.my.fctriPad 1:.clusterid.fctr4 1.233498e-02
## prdline.my.fctriPadmini:.clusterid.fctr5 8.680101e-03
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 3+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## importance
## biddable 1.000000e+02
## prdline.my.fctriPadAir 6.017061e+01
## idseq.my 5.834247e+01
## condition.fctrNew 3.187377e+01
## prdline.my.fctriPadmini 2+ 1.584214e+01
## prdline.my.fctriPad 1 1.269167e+01
## storage.fctr16 1.111043e+01
## condition.fctrFor parts or not working 9.445925e+00
## storage.fctr64 8.479953e+00
## D.TfIdf.sum.stem.stop.Ratio 7.845998e+00
## color.fctrWhite 6.841606e+00
## D.ratio.sum.TfIdf.nwrds 6.639130e+00
## color.fctrGold 6.526682e+00
## cellular.fctr1 6.328521e+00
## D.ratio.nstopwrds.nwrds 6.086762e+00
## carrier.fctrUnknown 5.477986e+00
## color.fctrSpace Gray 5.299191e+00
## condition.fctrNew other (see details) 4.690868e+00
## prdline.my.fctriPad 3+ 4.038812e+00
## color.fctrBlack 3.712488e+00
## storage.fctr32 3.553819e+00
## carrier.fctrAT&T 3.424452e+00
## D.TfIdf.sum.post.stop 3.185491e+00
## cellular.fctrUnknown 3.045839e+00
## carrier.fctrVerizon 2.871059e+00
## storage.fctrUnknown 2.855441e+00
## D.sum.TfIdf 2.633770e+00
## D.TfIdf.sum.post.stem 2.581885e+00
## D.nuppr.log 2.449205e+00
## prdline.my.fctriPad 2 2.390203e+00
## prdline.my.fctrUnknown:.clusterid.fctr2 1.933986e+00
## prdline.my.fctriPadmini 1.785961e+00
## carrier.fctrSprint 1.755063e+00
## D.nchrs.log 1.717401e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 1.612254e+00
## D.nstopwrds.log 1.601153e+00
## D.ndgts.log 1.499496e+00
## D.npnct13.log 1.470214e+00
## D.nwrds.log 1.414854e+00
## D.npnct11.log 1.354603e+00
## carrier.fctrT-Mobile 1.040226e+00
## D.terms.n.post.stem 9.785629e-01
## D.terms.n.post.stop 9.386855e-01
## D.terms.n.post.stem.log 9.136787e-01
## D.nwrds.unq.log 8.807968e-01
## D.terms.n.post.stop.log 8.766611e-01
## D.npnct01.log 8.763425e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 7.410079e-01
## condition.fctrManufacturer refurbished 7.341349e-01
## condition.fctrSeller refurbished 7.139955e-01
## D.terms.n.stem.stop.Ratio 4.135959e-01
## carrier.fctrOther 3.500180e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 2.412510e-01
## D.npnct15.log 2.138585e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 2.006444e-01
## D.npnct16.log 1.985543e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 1.963476e-01
## prdline.my.fctriPadAir:.clusterid.fctr4 1.913993e-01
## D.npnct06.log 1.899101e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.896488e-01
## D.npnct12.log 1.830369e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.582730e-01
## D.npnct24.log 1.582566e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 1.462750e-01
## D.npnct14.log 1.302889e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 1.257171e-01
## D.npnct08.log 1.139584e-01
## D.npnct03.log 1.084861e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 9.910833e-02
## prdline.my.fctriPad 2:.clusterid.fctr2 8.053090e-02
## prdline.my.fctriPad 2:.clusterid.fctr4 7.364192e-02
## prdline.my.fctriPad 1:.clusterid.fctr2 3.848913e-02
## prdline.my.fctriPad 2:.clusterid.fctr3 3.822434e-02
## prdline.my.fctriPad 1:.clusterid.fctr3 3.177592e-02
## prdline.my.fctriPad 2:.clusterid.fctr5 3.162311e-02
## prdline.my.fctrUnknown:.clusterid.fctr3 2.518860e-02
## D.npnct05.log 2.124534e-02
## prdline.my.fctriPad 1:.clusterid.fctr4 1.233498e-02
## prdline.my.fctriPadmini:.clusterid.fctr5 8.680101e-03
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 3+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## Final.rf.importance
## biddable 1.000000e+02
## prdline.my.fctriPadAir 6.017061e+01
## idseq.my 5.834247e+01
## condition.fctrNew 3.187377e+01
## prdline.my.fctriPadmini 2+ 1.584214e+01
## prdline.my.fctriPad 1 1.269167e+01
## storage.fctr16 1.111043e+01
## condition.fctrFor parts or not working 9.445925e+00
## storage.fctr64 8.479953e+00
## D.TfIdf.sum.stem.stop.Ratio 7.845998e+00
## color.fctrWhite 6.841606e+00
## D.ratio.sum.TfIdf.nwrds 6.639130e+00
## color.fctrGold 6.526682e+00
## cellular.fctr1 6.328521e+00
## D.ratio.nstopwrds.nwrds 6.086762e+00
## carrier.fctrUnknown 5.477986e+00
## color.fctrSpace Gray 5.299191e+00
## condition.fctrNew other (see details) 4.690868e+00
## prdline.my.fctriPad 3+ 4.038812e+00
## color.fctrBlack 3.712488e+00
## storage.fctr32 3.553819e+00
## carrier.fctrAT&T 3.424452e+00
## D.TfIdf.sum.post.stop 3.185491e+00
## cellular.fctrUnknown 3.045839e+00
## carrier.fctrVerizon 2.871059e+00
## storage.fctrUnknown 2.855441e+00
## D.sum.TfIdf 2.633770e+00
## D.TfIdf.sum.post.stem 2.581885e+00
## D.nuppr.log 2.449205e+00
## prdline.my.fctriPad 2 2.390203e+00
## prdline.my.fctrUnknown:.clusterid.fctr2 1.933986e+00
## prdline.my.fctriPadmini 1.785961e+00
## carrier.fctrSprint 1.755063e+00
## D.nchrs.log 1.717401e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 1.612254e+00
## D.nstopwrds.log 1.601153e+00
## D.ndgts.log 1.499496e+00
## D.npnct13.log 1.470214e+00
## D.nwrds.log 1.414854e+00
## D.npnct11.log 1.354603e+00
## carrier.fctrT-Mobile 1.040226e+00
## D.terms.n.post.stem 9.785629e-01
## D.terms.n.post.stop 9.386855e-01
## D.terms.n.post.stem.log 9.136787e-01
## D.nwrds.unq.log 8.807968e-01
## D.terms.n.post.stop.log 8.766611e-01
## D.npnct01.log 8.763425e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 7.410079e-01
## condition.fctrManufacturer refurbished 7.341349e-01
## condition.fctrSeller refurbished 7.139955e-01
## D.terms.n.stem.stop.Ratio 4.135959e-01
## carrier.fctrOther 3.500180e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 2.412510e-01
## D.npnct15.log 2.138585e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 2.006444e-01
## D.npnct16.log 1.985543e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 1.963476e-01
## prdline.my.fctriPadAir:.clusterid.fctr4 1.913993e-01
## D.npnct06.log 1.899101e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.896488e-01
## D.npnct12.log 1.830369e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.582730e-01
## D.npnct24.log 1.582566e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 1.462750e-01
## D.npnct14.log 1.302889e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 1.257171e-01
## D.npnct08.log 1.139584e-01
## D.npnct03.log 1.084861e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 9.910833e-02
## prdline.my.fctriPad 2:.clusterid.fctr2 8.053090e-02
## prdline.my.fctriPad 2:.clusterid.fctr4 7.364192e-02
## prdline.my.fctriPad 1:.clusterid.fctr2 3.848913e-02
## prdline.my.fctriPad 2:.clusterid.fctr3 3.822434e-02
## prdline.my.fctriPad 1:.clusterid.fctr3 3.177592e-02
## prdline.my.fctriPad 2:.clusterid.fctr5 3.162311e-02
## prdline.my.fctrUnknown:.clusterid.fctr3 2.518860e-02
## D.npnct05.log 2.124534e-02
## prdline.my.fctriPad 1:.clusterid.fctr4 1.233498e-02
## prdline.my.fctriPadmini:.clusterid.fctr5 8.680101e-03
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 3+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00print("glb_newobs_df prediction stats:")## [1] "glb_newobs_df prediction stats:"print(myplot_histogram(glb_newobs_df, paste0(glb_rsp_var_out, glb_fin_mdl_id)))## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
if (glb_is_classification)
print(table(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)]))
# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
if (length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
if (length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
if (length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
rm(submit_df, tmp_OOBobs_df)
# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 16 predict.data.new 9 0 298.560 308.955 10.395
## 17 display.session.info 10 0 308.955 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 114.634 246.100 131.466
## 5 extract.features 3 0 20.089 88.682 68.593
## 12 fit.models 7 2 246.100 269.152 23.052
## 14 fit.data.training 8 0 276.620 292.946 16.326
## 10 fit.models 7 0 98.555 114.633 16.078
## 16 predict.data.new 9 0 298.560 308.955 10.395
## 13 fit.models 7 3 269.153 276.619 7.466
## 1 import.data 1 0 10.137 16.272 6.136
## 15 fit.data.training 8 1 292.947 298.560 5.613
## 7 manage.missing.data 4 1 89.749 94.619 4.870
## 8 select.features 5 0 94.619 97.984 3.365
## 2 inspect.data 2 0 16.273 18.690 2.417
## 6 cluster.data 4 0 88.683 89.748 1.065
## 4 transform.data 2 2 19.371 20.088 0.717
## 3 scrub.data 2 1 18.691 19.371 0.680
## 9 partition.data.training 6 0 97.985 98.554 0.570
## duration
## 11 131.466
## 5 68.593
## 12 23.052
## 14 16.326
## 10 16.078
## 16 10.395
## 13 7.466
## 1 6.135
## 15 5.613
## 7 4.870
## 8 3.365
## 2 2.417
## 6 1.065
## 4 0.717
## 3 0.680
## 9 0.569
## [1] "Total Elapsed Time: 308.955 secs"
## R version 3.2.1 (2015-06-18)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.4 (Yosemite)
##
## locale:
## [1] C/en_US.UTF-8/C/C/C/en_US.UTF-8
##
## attached base packages:
## [1] tcltk grid parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] randomForest_4.6-10 glmnet_2.0-2 arm_1.8-6
## [4] lme4_1.1-8 Matrix_1.2-2 MASS_7.3-43
## [7] rpart.plot_1.5.2 rpart_4.1-10 tidyr_0.2.0
## [10] entropy_1.2.1 dynamicTreeCut_1.62 proxy_0.4-15
## [13] reshape2_1.4.1 sqldf_0.4-10 RSQLite_1.0.0
## [16] DBI_0.3.1 tm_0.6-2 NLP_0.1-8
## [19] stringr_1.0.0 gsubfn_0.6-6 proto_0.3-10
## [22] mgcv_1.8-7 nlme_3.1-121 dplyr_0.4.2
## [25] plyr_1.8.3 gdata_2.17.0 doMC_1.3.3
## [28] iterators_1.0.7 foreach_1.4.2 doBy_4.5-13
## [31] survival_2.38-3 caret_6.0-52 ggplot2_1.0.1
## [34] lattice_0.20-33
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.0 gtools_3.5.0 assertthat_0.1
## [4] digest_0.6.8 slam_0.1-32 R6_2.1.0
## [7] BradleyTerry2_1.0-6 chron_2.3-47 stats4_3.2.1
## [10] coda_0.17-1 evaluate_0.7 lazyeval_0.1.10
## [13] minqa_1.2.4 SparseM_1.6 car_2.0-25
## [16] nloptr_1.0.4 rmarkdown_0.7 labeling_0.3
## [19] splines_3.2.1 munsell_0.4.2 compiler_3.2.1
## [22] htmltools_0.2.6 nnet_7.3-10 codetools_0.2-14
## [25] brglm_0.5-9 gtable_0.1.2 magrittr_1.5
## [28] formatR_1.2 scales_0.2.5 stringi_0.5-5
## [31] RColorBrewer_1.1-2 tools_3.2.1 abind_1.4-3
## [34] pbkrtest_0.4-2 yaml_2.1.13 colorspace_1.2-6
## [37] knitr_1.10.5 quantreg_5.11